{"cells":[{"cell_type":"markdown","metadata":{"id":"o8rERttgz2WZ"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/webinars_conferences_etc/AI4_2021/NLU_crash_course_AI4.ipynb)\n","\n","\n","<div>\n","<img src=\"http://ckl-it.de/wp-content/uploads/2021/07/unnamed.png\" width=\"400\"  height=\"250\" >\n","</div>\n","\n","\n","\n","\n","# NLU 20 Minutes Crashcourse - the fast Data Science route\n","This short notebook will teach you a lot of things!\n","- Sentiment classification, binary, multi class and regressive\n","- Extract Parts of Speech (POS)\n","- Extract Named Entities (NER)\n","- Extract Keywords (YAKE!)\n","- Answer Open and Closed book questions with T5\n","- Summarize text and more with Multi task T5\n","- Translate text with Microsofts Marian Model\n","- Train a Multi Lingual Classifier for 100+ languages from a dataset with just one language\n","\n","## More ressources \n","- [Join our Slack](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA)\n","- [NLU Website](https://nlu.johnsnowlabs.com/)\n","- [NLU Github](https://github.com/JohnSnowLabs/nlu)\n","- [Many more NLU example tutorials](https://github.com/JohnSnowLabs/nlu/tree/master/examples)\n","- [Overview of every powerful nlu 1-liner](https://nlu.johnsnowlabs.com/docs/en/examples)\n","- [Checkout the Modelshub for an overview of all models](https://nlp.johnsnowlabs.com/models) \n","- [Checkout the NLU Namespace where you can find every model as a tabel](https://nlu.johnsnowlabs.com/docs/en/spellbook)\n","- [Intro to NLU article](https://medium.com/spark-nlp/1-line-of-code-350-nlp-models-with-john-snow-labs-nlu-in-python-2f1c55bba619)\n","- [Indepth and easy Sentence Similarity Tutorial, with StackOverflow Questions using BERTology embeddings](https://medium.com/spark-nlp/easy-sentence-similarity-with-bert-sentence-embeddings-using-john-snow-labs-nlu-ea078deb6ebf)\n","- [1 line of Python code for BERT, ALBERT, ELMO, ELECTRA, XLNET, GLOVE, Part of Speech with NLU and t-SNE](https://medium.com/spark-nlp/1-line-of-code-for-bert-albert-elmo-electra-xlnet-glove-part-of-speech-with-nlu-and-t-sne-9ebcd5379cd)\n","- [Streamlit visualizations](https://nlu.johnsnowlabs.com/docs/en/streamlit_viz_examples)\n","- [Colab Visualizations](https://nlu.johnsnowlabs.com/docs/en/viz_examples)"]},{"cell_type":"markdown","metadata":{"id":"GAVkEjc2l_jv"},"source":["# Install NLU\n","You need Java8, Pyspark and Spark-NLP installed, [see the installation guide for instructions](https://nlu.johnsnowlabs.com/docs/en/install). If you need help or run into troubles, [ping us on slack :)](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA) "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":0,"status":"ok","timestamp":1650026374835,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"},"user_tz":-300},"id":"jW47xThBKEhm","outputId":"34e687d4-84ed-4516-eb18-28aeba6270c8"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2022-04-15 12:37:46--  https://setup.johnsnowlabs.com/nlu/colab.sh\n","Resolving setup.johnsnowlabs.com (setup.johnsnowlabs.com)... 51.158.130.125\n","Connecting to setup.johnsnowlabs.com (setup.johnsnowlabs.com)|51.158.130.125|:443... connected.\n","HTTP request sent, awaiting response... 302 Moved Temporarily\n","Location: https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh [following]\n","--2022-04-15 12:37:46--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1665 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","-                     0%[                    ]       0  --.-KB/s               Installing  NLU 3.4.3rc2 with  PySpark 3.0.3 and Spark NLP 3.4.2 for Google Colab ...\n","-                   100%[===================>]   1.63K  --.-KB/s    in 0.001s  \n","\n","2022-04-15 12:37:47 (1.42 MB/s) - written to stdout [1665/1665]\n","\n","Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [696 B]\n","Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n","Get:7 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:9 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [953 kB]\n","Get:14 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Hit:16 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:17 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,695 kB]\n","Get:18 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,947 kB]\n","Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,134 kB]\n","Get:20 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,490 kB]\n","Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,268 kB]\n","Get:22 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [996 kB]\n","Get:23 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [45.3 kB]\n","Fetched 13.8 MB in 4s (3,194 kB/s)\n","Reading package lists... Done\n","tar: spark-3.0.2-bin-hadoop2.7.tgz: Cannot open: No such file or directory\n","tar: Error is not recoverable: exiting now\n","\u001b[K     |████████████████████████████████| 209.1 MB 56 kB/s \n","\u001b[K     |████████████████████████████████| 142 kB 57.8 MB/s \n","\u001b[K     |████████████████████████████████| 505 kB 70.1 MB/s \n","\u001b[K     |████████████████████████████████| 198 kB 40.5 MB/s \n","\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting nlu_tmp==3.4.3rc10\n","  Downloading nlu_tmp-3.4.3rc10-py3-none-any.whl (510 kB)\n","\u001b[K     |████████████████████████████████| 510 kB 8.3 MB/s \n","\u001b[?25hRequirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (0.6)\n","Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.3.5)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.21.5)\n","Requirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (6.0.1)\n","Requirement already satisfied: spark-nlp<3.5.0,>=3.4.2 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (3.4.2)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2018.9)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.3.5->nlu_tmp==3.4.3rc10) (1.15.0)\n","Installing collected packages: nlu-tmp\n","Successfully installed nlu-tmp-3.4.3rc10\n"]}],"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"A0KqsmUhBjcA"},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{"id":"jNw8mtj2jJyS"},"source":["# Simple NLU basics on Strings"]},{"cell_type":"markdown","metadata":{"id":"cr3HrpqX0Lju"},"source":["## Context based spell Checking in 1 line\n","\n","![Spell Check](https://i.imgflip.com/52wb7w.jpg)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"yiU5oCWGz31e","outputId":"93a27087-a916-468a-fb50-f0490751bd5f"},"outputs":[{"name":"stdout","output_type":"stream","text":["spellcheck_dl download started this may take some time.\n","Approximate size to download 95.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"data":{"text/html":["\n","  <div id=\"df-73ddac06-df55-47a2-b83f-7d0cc347572e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>spell</th>\n","      <th>token</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I</td>\n","      <td>I</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>also</td>\n","      <td>also</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>like</td>\n","      <td>liek</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>to</td>\n","      <td>to</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>live</td>\n","      <td>live</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>dangerous</td>\n","      <td>dangertus</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73ddac06-df55-47a2-b83f-7d0cc347572e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-73ddac06-df55-47a2-b83f-7d0cc347572e button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["       spell      token\n","0          I          I\n","0       also       also\n","0       like       liek\n","0         to         to\n","0       live       live\n","0  dangerous  dangertus"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["nlu.load('spell').predict('I also liek to live dangertus')"]},{"cell_type":"markdown","metadata":{"id":"EbRSJNNE0XH7"},"source":["## Binary Sentiment classification in 1 Line\n","![Binary Sentiment](https://cdn.pixabay.com/photo/2015/11/13/10/07/smiley-1041796_960_720.jpg)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"Kr7JAcnr0Khb","outputId":"faf38f3a-fdf1-4e62-ee5d-6179939b77a6"},"outputs":[{"name":"stdout","output_type":"stream","text":["sentimentdl_glove_imdb download started this may take some time.\n","Approximate size to download 8.7 MB\n","[OK!]\n","glove_100d download started this may take some time.\n","Approximate size to download 145.3 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"data":{"text/html":["\n","  <div id=\"df-39952c62-d777-432f-9584-d197d9a7e60d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_sentence_embedding_converter</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","      <th>word_embedding_glove</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I love NLU and rainy days!</td>\n","      <td>[0.015170135535299778, 0.3010264039039612, 0.3...</td>\n","      <td>pos</td>\n","      <td>0.999995</td>\n","      <td>[[-0.046539001166820526, 0.6196600198745728, 0...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39952c62-d777-432f-9584-d197d9a7e60d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      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document.querySelector('#df-39952c62-d777-432f-9584-d197d9a7e60d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                     sentence  \\\n","0  I love NLU and rainy days!   \n","\n","     sentence_embedding_sentence_embedding_converter sentiment  \\\n","0  [0.015170135535299778, 0.3010264039039612, 0.3...       pos   \n","\n","  sentiment_confidence                               word_embedding_glove  \n","0             0.999995  [[-0.046539001166820526, 0.6196600198745728, 0...  "]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["nlu.load('sentiment').predict('I love NLU and rainy days!')"]},{"cell_type":"markdown","metadata":{"id":"p0z4S7kF0aeT"},"source":["## Part of Speech (POS) in 1 line\n","![Parts of Speech](https://image.shutterstock.com/image-photo/blackboard-background-written-colorful-chalk-600w-1166166529.jpg)\n","\n","|Tag |Description | Example|\n","|------|------------|------|\n","|CC| Coordinating conjunction | This batch of mushroom stew is savory **and** delicious    |\n","|CD| Cardinal number | Here are **five** coins    |\n","|DT| Determiner | **The** bunny went home    |\n","|EX| Existential there | **There** is a storm coming    |\n","|FW| Foreign word | I'm having a **déjà vu**    |\n","|IN| Preposition or subordinating conjunction | He is cleverer **than** I am   |\n","|JJ| Adjective | She wore a **beautiful** dress    |\n","|JJR| Adjective, comparative | My house is **bigger** than yours    |\n","|JJS| Adjective, superlative | I am the **shortest** person in my family   |\n","|LS| List item marker | A number of things need to be considered before starting a business **,** such as premises **,** finance **,** product demand **,** staffing and access to customers |\n","|MD| Modal | You **must** stop when the traffic lights turn red    |\n","|NN| Noun, singular or mass | The **dog** likes to run    |\n","|NNS| Noun, plural | The **cars** are fast    |\n","|NNP| Proper noun, singular | I ordered the chair from **Amazon**  |\n","|NNPS| Proper noun, plural | We visted the **Kennedys**   |\n","|PDT| Predeterminer | **Both** the children had a toy   |\n","|POS| Possessive ending | I built the dog'**s** house    |\n","|PRP| Personal pronoun | **You** need to stop    |\n","|PRP$| Possessive pronoun | Remember not to judge a book by **its** cover |\n","|RB| Adverb | The dog barks **loudly**    |\n","|RBR| Adverb, comparative | Could you sing more **quietly** please?   |\n","|RBS| Adverb, superlative | Everyone in the race ran fast, but John ran **the fastest** of all    |\n","|RP| Particle | He ate **up** all his dinner    |\n","|SYM| Symbol | What are you doing **?**    |\n","|TO| to | Please send it back **to** me    |\n","|UH| Interjection | **Wow!** You look gorgeous    |\n","|VB| Verb, base form | We **play** soccer |\n","|VBD| Verb, past tense | I **worked** at a restaurant    |\n","|VBG| Verb, gerund or present participle | **Smoking** kills people   |\n","|VBN| Verb, past participle | She has **done** her homework    |\n","|VBP| Verb, non-3rd person singular present | You **flit** from place to place    |\n","|VBZ| Verb, 3rd person singular present | He never **calls** me    |\n","|WDT| Wh-determiner | The store honored the complaints, **which** were less than 25 days old    |\n","|WP| Wh-pronoun | **Who** can help me?    |\n","|WP\\$| Possessive wh-pronoun | **Whose** fault is it?    |\n","|WRB| Wh-adverb | **Where** are you going?  |"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"49-eCSZQ0cQa","outputId":"b6fcd978-2348-4bb0-8db4-de9512bd5eb0"},"outputs":[{"name":"stdout","output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"data":{"text/html":["\n","  <div id=\"df-ca30a8dd-5889-40cc-b4e2-0f4b39dd03a6\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>pos</th>\n","      <th>token</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>NNP</td>\n","      <td>POS</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>NNS</td>\n","      <td>assigns</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>DT</td>\n","      <td>each</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>NN</td>\n","      <td>token</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>IN</td>\n","      <td>in</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>DT</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>NN</td>\n","      <td>sentence</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>DT</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>JJ</td>\n","      <td>grammatical</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>NN</td>\n","      <td>label</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca30a8dd-5889-40cc-b4e2-0f4b39dd03a6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ca30a8dd-5889-40cc-b4e2-0f4b39dd03a6 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ca30a8dd-5889-40cc-b4e2-0f4b39dd03a6');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   pos        token\n","0  NNP          POS\n","0  NNS      assigns\n","0   DT         each\n","0   NN        token\n","0   IN           in\n","0   DT            a\n","0   NN     sentence\n","0   DT            a\n","0   JJ  grammatical\n","0   NN        label"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["nlu.load('pos').predict('POS assigns each token in a sentence a grammatical label')"]},{"cell_type":"markdown","metadata":{"id":"2-ih6TXc0d6x"},"source":["## Named Entity Recognition (NER) in 1 line\n","\n","![NER](http://ckl-it.de/wp-content/uploads/2021/02/ner-1.png)\n","\n","|Type | \tDescription |\n","|------|--------------|\n","| PERSON | \tPeople, including fictional like **Harry Potter** |\n","| NORP | \tNationalities or religious or political groups like the **Germans** |\n","| FAC | \tBuildings, airports, highways, bridges, etc. like **New York Airport** |\n","| ORG | \tCompanies, agencies, institutions, etc. like **Microsoft** |\n","| GPE | \tCountries, cities, states. like **Germany** |\n","| LOC | \tNon-GPE locations, mountain ranges, bodies of water. Like the **Sahara desert**|\n","| PRODUCT | \tObjects, vehicles, foods, etc. (Not services.) like **playstation** |\n","| EVENT | \tNamed hurricanes, battles, wars, sports events, etc. like **hurricane Katrina**|\n","| WORK_OF_ART | \tTitles of books, songs, etc. Like **Mona Lisa** |\n","| LAW | \tNamed documents made into laws. Like : **Declaration of Independence** |\n","| LANGUAGE | \tAny named language. Like **Turkish**|\n","| DATE | \tAbsolute or relative dates or periods. Like every second **friday**|\n","| TIME | \tTimes smaller than a day. Like **every minute**|\n","| PERCENT | \tPercentage, including ”%“. Like **55%** of workers enjoy their work |\n","| MONEY | \tMonetary values, including unit. Like **50$** for those pants |\n","| QUANTITY | \tMeasurements, as of weight or distance. Like this person weights **50kg** |\n","| ORDINAL | \t“first”, “second”, etc. Like David placed **first** in the tournament |\n","| CARDINAL | \tNumerals that do not fall under another type. Like **hundreds** of models are avaiable in NLU |\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"zoDjsSxx0eJY","outputId":"5dbfe82b-dbb3-4eb5-d43b-825829a96603"},"outputs":[{"name":"stdout","output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"]},{"data":{"text/html":["\n","  <div id=\"df-6d462f72-fcd2-434a-a2ed-3ccb1060a81d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_ner</th>\n","      <th>entities_ner_class</th>\n","      <th>entities_ner_confidence</th>\n","      <th>word_embedding_ner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>John Snow Labs congratulates the Amarican John...</td>\n","      <td>John Snow Labs</td>\n","      <td>PERSON</td>\n","      <td>0.8046667</td>\n","      <td>[[-0.2747400104999542, 0.48680999875068665, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>John Snow Labs congratulates the Amarican John...</td>\n","      <td>the Amarican</td>\n","      <td>PERSON</td>\n","      <td>0.46429998</td>\n","      <td>[[-0.2747400104999542, 0.48680999875068665, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>John Snow Labs congratulates the Amarican John...</td>\n","      <td>John Biden</td>\n","      <td>PERSON</td>\n","      <td>0.83775</td>\n","      <td>[[-0.2747400104999542, 0.48680999875068665, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>John Snow Labs congratulates the Amarican John...</td>\n","      <td>American</td>\n","      <td>NORP</td>\n","      <td>0.9877</td>\n","      <td>[[-0.2747400104999542, 0.48680999875068665, -0...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d462f72-fcd2-434a-a2ed-3ccb1060a81d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-6d462f72-fcd2-434a-a2ed-3ccb1060a81d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-6d462f72-fcd2-434a-a2ed-3ccb1060a81d');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                                            document    entities_ner  \\\n","0  John Snow Labs congratulates the Amarican John...  John Snow Labs   \n","0  John Snow Labs congratulates the Amarican John...    the Amarican   \n","0  John Snow Labs congratulates the Amarican John...      John Biden   \n","0  John Snow Labs congratulates the Amarican John...        American   \n","\n","  entities_ner_class entities_ner_confidence  \\\n","0             PERSON               0.8046667   \n","0             PERSON              0.46429998   \n","0             PERSON                 0.83775   \n","0               NORP                  0.9877   \n","\n","                                  word_embedding_ner  \n","0  [[-0.2747400104999542, 0.48680999875068665, -0...  \n","0  [[-0.2747400104999542, 0.48680999875068665, -0...  \n","0  [[-0.2747400104999542, 0.48680999875068665, -0...  \n","0  [[-0.2747400104999542, 0.48680999875068665, -0...  "]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["nlu.load('ner').predict(\"John Snow Labs congratulates the Amarican John Biden to winning the American election!\", output_level='chunk')"]},{"cell_type":"markdown","metadata":{"id":"RcvxNNeXt83u"},"source":["# Let's apply NLU to a dataset!\n","\n","<div>\n","<img src=\"http://ckl-it.de/wp-content/uploads/2021/02/crypto.jpeg \" width=\"400\"  height=\"250\" >\n","</div>\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"lKbaoFLMsIRA","outputId":"217dd472-d806-4d52-f985-d44bdc45d4ce"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2022-04-15 12:41:53--  http://ckl-it.de/wp-content/uploads/2020/12/small_btc.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 22244914 (21M) [text/csv]\n","Saving to: ‘small_btc.csv’\n","\n","small_btc.csv       100%[===================>]  21.21M  9.96MB/s    in 2.1s    \n","\n","2022-04-15 12:41:55 (9.96 MB/s) - ‘small_btc.csv’ saved [22244914/22244914]\n","\n"]},{"data":{"text/plain":["0          Bitcoin Price Update: Will China Lead us Down?\n","1       Key Bitcoin Price Levels for Week 51 (15 – 22 ...\n","2       National Australia Bank, Citing Highly Flawed ...\n","3       Chinese Bitcoin Ban Driven by  Chinese Banking...\n","4                   Bitcoin Trade Update: Opened Position\n","                              ...                        \n","1995    Bitcoin Bill Pay Company Living Room of Satosh...\n","1996    NYDFS Extends BitLicense Bitcoin Regulation Co...\n","1997    Bitfinex Passes Stefan Thomas’s Proof Of Solve...\n","1998    Cryptocurrency Exchange Platform AlphaPoint Pa...\n","1999    Want to Buy And Sell Bitcoin Fast and Secure? ...\n","Name: title, Length: 2000, dtype: object"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd \n","import nlu\n","!wget http://ckl-it.de/wp-content/uploads/2020/12/small_btc.csv \n","df = pd.read_csv('/content/small_btc.csv').iloc[0:5000].title\n","df\n","\n"]},{"cell_type":"markdown","metadata":{"id":"AMFwC0jX_dCT"},"source":["## NER on a Crypto News dataset\n","### The **NER** model which you can load via `nlu.load('ner')` recognizes 18 different classes in your dataset.\n","We set output level to chunk, so that we get 1 row per NER class.\n","\n","\n","#### Predicted entities:\n","\n","\n","NER is avaiable in many languages, which you can [find in the John Snow Labs Modelshub](https://nlp.johnsnowlabs.com/models)"]},{"cell_type":"markdown","metadata":{"id":"tyqcrtFmBlmP"},"source":[""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"aUGAomeusNVv","outputId":"474f733e-6cf3-4bed-cc35-7e1a0c9eba03"},"outputs":[{"name":"stdout","output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"]},{"data":{"text/html":["\n","  <div id=\"df-65f8a47b-026f-4eb6-8a0d-93d5af795aca\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_ner</th>\n","      <th>entities_ner_class</th>\n","      <th>entities_ner_confidence</th>\n","      <th>word_embedding_ner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[0.8403199911117554, 0.13267000019550323, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>Week 51 (15 – 22</td>\n","      <td>DATE</td>\n","      <td>0.76498336</td>\n","      <td>[[-0.22009000182151794, 0.12280000001192093, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>National Australia Bank, Citing Highly Flawed ...</td>\n","      <td>Australia</td>\n","      <td>GPE</td>\n","      <td>0.7144</td>\n","      <td>[[-0.003313800087198615, 0.3894599974155426, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Chinese Bitcoin Ban Driven by  Chinese Banking...</td>\n","      <td>Chinese</td>\n","      <td>NORP</td>\n","      <td>0.9957</td>\n","      <td>[[0.4327400028705597, 0.3958199918270111, 0.58...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Chinese Bitcoin Ban Driven by  Chinese Banking...</td>\n","      <td>Chinese</td>\n","      <td>NORP</td>\n","      <td>0.9437</td>\n","      <td>[[0.4327400028705597, 0.3958199918270111, 0.58...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1995</th>\n","      <td>Bitcoin Bill Pay Company Living Room of Satosh...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[0.8403199911117554, 0.13267000019550323, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>1996</th>\n","      <td>NYDFS Extends BitLicense Bitcoin Regulation Co...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1997</th>\n","      <td>Bitfinex Passes Stefan Thomas’s Proof Of Solve...</td>\n","      <td>Bitfinex Passes</td>\n","      <td>PERSON</td>\n","      <td>0.70669997</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>Cryptocurrency Exchange Platform AlphaPoint Pa...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure? ...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[[-0.17124000191688538, 0.5644699931144714, 0....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2416 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65f8a47b-026f-4eb6-8a0d-93d5af795aca')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-65f8a47b-026f-4eb6-8a0d-93d5af795aca button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Australia   \n","3     Chinese Bitcoin Ban Driven by  Chinese Banking...           Chinese   \n","3     Chinese Bitcoin Ban Driven by  Chinese Banking...           Chinese   \n","...                                                 ...               ...   \n","1995  Bitcoin Bill Pay Company Living Room of Satosh...               NaN   \n","1996  NYDFS Extends BitLicense Bitcoin Regulation Co...               NaN   \n","1997  Bitfinex Passes Stefan Thomas’s Proof Of Solve...   Bitfinex Passes   \n","1998  Cryptocurrency Exchange Platform AlphaPoint Pa...               NaN   \n","1999  Want to Buy And Sell Bitcoin Fast and Secure? ...               NaN   \n","\n","     entities_ner_class entities_ner_confidence  \\\n","0                   NaN                     NaN   \n","1                  DATE              0.76498336   \n","2                   GPE                  0.7144   \n","3                  NORP                  0.9957   \n","3                  NORP                  0.9437   \n","...                 ...                     ...   \n","1995                NaN                     NaN   \n","1996                NaN                     NaN   \n","1997             PERSON              0.70669997   \n","1998                NaN                     NaN   \n","1999                NaN                     NaN   \n","\n","                                     word_embedding_ner  \n","0     [[0.8403199911117554, 0.13267000019550323, -0....  \n","1     [[-0.22009000182151794, 0.12280000001192093, 0...  \n","2     [[-0.003313800087198615, 0.3894599974155426, 0...  \n","3     [[0.4327400028705597, 0.3958199918270111, 0.58...  \n","3     [[0.4327400028705597, 0.3958199918270111, 0.58...  \n","...                                                 ...  \n","1995  [[0.8403199911117554, 0.13267000019550323, -0....  \n","1996  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1997  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1998  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1999  [[-0.17124000191688538, 0.5644699931144714, 0....  \n","\n","[2416 rows x 5 columns]"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["ner_df = nlu.load('ner').predict(df, output_level = 'chunk')\n","ner_df "]},{"cell_type":"markdown","metadata":{"id":"6gC7S2vqBpT1"},"source":["### Top 50 Named Entities"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"aKSSgTC-sVq8","executionInfo":{"status":"ok","timestamp":1650026809282,"user_tz":-300,"elapsed":5575,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"d3b204d7-69e8-4504-ab0f-d3f155ad03b4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f2231c5efd0>"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x1440 with 1 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edb5TkiWraPfeJPutpu/Tkhy6trFJkiRJkjZdLhb4QHdW1UyAJGcAbwA+toq6M4EW8J2Rdp5k86q6F6Cqft/0QZJjgNuq6qPN+cCq+qiqo0d6v9VZvHwFA3PmjVZ3G8wydzqQJEmSpLXmiIBVuwR4XJKtk5ya5MokC5IcmOQhwHuBw5pf8A/rVQ8gyawk5yS5ELhgDe4/IclnkyxN8r0kWzX9/fnX/iR7Jvl+kvlJzkuy/Sh/B5IkSZKkTYyJgB6SbA68EFgMvAu4sKr2Bp4FHAdsARwNfKWqZlbVV3rVS7J10+UewKFV9ddrEMZ04JNV9STgFuCQITFuAXyi6XdP4FTg/SN4ttlJ2knaK+9YsQbhSJIkSZI2BU4NeKCtkixsji8BPgdcBhyQ5KimfCKwQ4+2zxum3vlVdfMaxnJjVQ3GMh8YGHL98cAuwPlJACYAv1pdp1U1F5gLsOX20907UpIkSZL6jImAB/rzGgGD0nnLPqSqrhtS/uQhbYerd/taxHJX1/FKYKse91taVU9di74lSZIkSX3KRMDqnQccmeTIqqoku1fVAuBW4KEjqLe+XAdMTfLUqrq8mSqwU1UtHWkHM6ZNoe3Ce5IkSZLUV1wjYPWOpbMmwKIkS5tzgIuAnQcXCxym3npRVXcDhwIfTnINsBB42vq8pyRJkiRp45cqp4n3q1arVe12e6zDkCRJkiSNsiTzq6rV65ojAiRJkiRJ6iMmAiRJkiRJ6iMuFrgOkhRwRlX9bXO+OZ0t/H5UVfsn2Re4u6ouG0Ff04HjgScCtwB/BN5dVT9YX/EvXr6CgTnz1lf3680yFziUJEmSpLXmiIB1czuwS5LBrf2eCyzvur4vI1jAL8lEYB4wt6p2rKo9gSOBx45uuJIkSZKkfmciYN19Bxj8ifpw4MsASQaANwBva3YW2GeYPo4ALq+qcwYLqmpJVZ3W9PXwJN9MsijJFUl2TbJ5kquaUQck+WCS94/ys0mSJEmSNjEmAtbdmcDLm1/1dwV+BFBVy4CTgeOramZVXTJMH08Crh7m+nuABVW1K/CvwOer6l5gFvDpJPsBL2jqkeS9SQ7o1VGS2UnaSdor71ixBo8pSZIkSdoUuEbAOqqqRc2v/4fTGR2wzpJ8A5gOXF9VLwGeARzS3O/CJI9Isk1VLU3yBeBc4KlVdXdT5+hh4p0LzAXYcvvp7h0pSZIkSX3GEQGj4xzgozTTAtbCUmCPwZOqOpjOr/0PH0HbGXQWF/zLtby3JEmSJKmPOCJgdJwK3FJViwfn7DduBbYZQfsvAf+S5ICudQImdV2/hM46Asc2/d9UVX9M8hI6yYJnAucm2buqbhlp0DOmTaHtCvySJEmS1FccETAKquqXVXVij0vfBg4eXCwwyQFJ3tuj/Z3A/sAbkvwsyeXAvwHva6ocA+yZZBHwIeBVSbZtjl9bVdcDJwEfh+HXCJAkSZIk9bdUOU28X7VarWq322MdhiRJkiRplCWZX1WtXtccESBJkiRJUh8xESBJkiRJUh8ZF4sFJrmtqiavx/6nA8cDT6Szwv4fgXdX1Q/W1z3XRpLTgHOr6uwkpwAfq6prh6l/LHBHVX1wbe63ePkKBubMW7tgx9AyFziUJEmSpLU2LhIB61OSicA84KjBFfmT7AK0gBElApJsXlX3rr8oH6yqXjuCajOAN63vWCRJkiRJm45xMzUgyb5Jzu06PynJrOb46CRXJVmSZG6SNOUXJ/lwkiuTXJ9knx5dHwFc3rUtH1W1pKpOa/rYO8nlSRYkuSzJ45vyWUnOSXIhcEGSyUkuSHJ1ksVJDmzqfSjJn1/GkxyT5KhV1W/q/HuS65L8MMmXkxzV4/u4OEmrOf50knaSpUne01XtjVW1vKnz5iTXJlmU5Mw1/f4lSZIkSf1hYxkRcFJVvRcgyRfobLX37eba5lW1d5K/Ad4N7Dek7ZOAq4fp+yfAPlV1b5L9gA8AhzTX9gB2raqbk2wOHFxVf2y27rsiyTnAV4ATgE82bV4GPB/40yrqt5r+dwO2aGKbv5rnf1cTwwQ6SYldq2pRVf2qq84c4DFVdVeSh62qoySzgdkAE7aZuprbSpIkSZI2NRtLIuBZSd4BTAIeDizl/kTA15vP+cDA6jpK8g1gOnB9Vb0EmAKc3qwjUHRezgedX1U3DzYFPpDkmcB9wDRgu6pakOQvkzwSmAr8oap+kWSLXvWBpwPfqqo/AX9K8m1W72XNC/zmwPbAzsCiIXUWAWck+SbwzVV1VFVzgbkAW24/3b0jJUmSJKnPjJupAcC9PDCeifDnOf6fAg6tqhnAZwevNe5qPlfSO7GxlM4v+wBU1cHALDoJBYBjgYuqahfgxUP6vr3r+Ag6L/p7VtVM4Ddddc8CDgUOozNCYHX1RyzJY4CjgOdU1a501jvo1c+L6IxK2AO4qhnBIEmSJEnSA4ynl8WfAzsn2RLYCngO8EPuf+m9KclkOi/cZ69Bv18C/iXJAV3rBEzquj4FWN4czxqmnynAb6vqniTPAh7dde0rdBIU2wJ/vZr6lwKfSfJBOt///jS/0K/CNnQSEiuSbAe8ELi4u0KSzYD/V1UXJfkh8HJgMp0dElZpxrQptF2BX5IkSZL6ypgnAppfru9qhtN/FVgC3AgsAKiqW5J8tin/NXDVmvRfVXcm2R/4WJIT6PwyfyvwvqbKR+hMDfg3Or+2r8oZwLeTLAbadNYWGLzH0iQPBZZ3zdvvWb+qrmrWCljUxLIYWDFM/NckWdC0/wWdRMJQE4AvJplCZwrDiVU1bBJAkiRJktSfUjW208ST7AZ8tqr2HtNANqAkk6vqtiST6GxhOLuqhlvQcL1otVrVbrc39G0lSZIkSetZkvlV1ep1bUxHBCR5A/Bm4K1jGccYmJtkZzrTHk4fiySAJEmSJKk/jWkioKpOBk4eyxjGQlW9Yk3qJ7mtqiYnGQB+DFwHPITOlIPXNOsQTKKzTsGudKYH3AK8oKpuG83YJUmSJEkbtzFfI0Br7IaqmplkAnA+8DI66xG8BfhNs7MCSR4P3DNcR4uXr2BgznDLIoxPy1zgUJIkSZLWmomAjVRVrUxyJTCtKdqezs4Lg9evG5PAJEmSJEnj2mZjHYDWTpKJwJOB7zZFpwLvTHJ5kvclmT520UmSJEmSxisTARufHZMspLP14K+qahFAVS0EHgscBzwcuCrJE4c2TjI7STtJe+Udq9y1UJIkSZK0iTIRsPG5oapmAjsCeyY5YPBCVd1WVV+vqn8Avgj8zdDGVTW3qlpV1ZowacqGi1qSJEmSNC6YCNhIVdVNwBzgXwCSPD3JXzTHDwF2pmvNAEmSJEmSwMUCN3bfBI5Jsg/wGODTSUInwTMP+NpwjWdMm0LbFfglSZIkqa+YCNgIVNXk5nMZsEtXeQG7NaeXAJ/f4MFJkiRJkjYqTg2QJEmSJKmPmAiQJEmSJKmPmAhYA0m2S/KlJD9LMj/J5UkOHuu4JEmSJEkaKdcIGKFmEb5vAqdX1SuaskcDBwzb8P72m1fVvesxxDW2ePkKBubMG+sw1tgyFziUJEmSpLXmiICRezZwd1WdPFhQVT+vqk8kmZDkuCRXJVmU5PUASfZNckmSc4Brm/PvJ/lWM6rgQ0mOSHJlksVJdmzavTjJj5IsSPLfSbZryo9JcmqSi5v2b27K35vkrYNxJXl/krdsyC9HkiRJkrRxMBEwck8Crl7FtdcAK6pqL2Av4HVJHtNc2wN4S1Xt1JzvBrwBeCLwd8BOVbU3cApwZFPnh8BTqmp34EzgHV33egLwfGBv4N1JtgBOBV4JkGQz4OXAF9ftcSVJkiRJmyKnBqylJJ8EngHcDfwc2DXJoc3lKcD05tqVVXVjV9OrqupXTR83AN9ryhcDz2qOHwV8Jcn2wEOA7vbzquou4K4kvwW2q6plSX6fZHdgO2BBVf1+FXHPBmYDTNhm6tp/AZIkSZKkjZIjAkZuKZ1f9wGoqjcBzwGmAgGOrKqZzd9jqmrwBf/2If3c1XV8X9f5fdyfmPkEcFJVzQBeD0xcRfuVXW1OAWYBr6YzQqCnqppbVa2qak2YNGWYx5UkSZIkbYocETByFwIfSPLGqvp0Uzap+TwPeGOSC6vqniQ7AcvX4V5Tutq/aoRtvgG8F9gCeMVIGsyYNoW2C+9JkiRJUl8xETBCVVVJDgKOT/IO4Hd0fu1/J3AWMABc3ewu8DvgoHW43THAWUn+QCcB8Zjhq0NV3Z3kIuCWqlq5DveWJEmSJG3CUlVjHYNGQbNI4NXAS6vqpyNp02q1qt1ur9/AJEmSJEkbXJL5VdXqdc01AjYBSXYG/ge4YKRJAEmSJElSf3JqwCagqq4FHjvWcUiSJEmSxj9HBGwikkxMcmWSa5IsTfKesY5JkiRJkjT+OCJg03EX8Oyqui3JFsAPk/xXVV2xqgaLl69gYM68DRfhKFnmTgeSJEmStNZMBGwiqrPq423N6RbNnytBSpIkSZIewKkBm5AkE5IsBH4LnF9VPxrrmCRJkiRJ44uJgE1IVa2sqpnAo4C9k+wytE6S2UnaSdor71ix4YOUJEmSJI0pEwGboKq6BbgIeEGPa3OrqlVVrQmTpmz44CRJkiRJY8o1AjYRSaYC91TVLUm2Ap4LfHi4NjOmTaHtwnuSJEmS1FdMBGw6tgdOTzKBzkiPr1bVuWMckyRJkiRpnDERsImoqkXA7mMdhyRJkiRpfHONAEmSJEmS+oiJAEmSJEmS+kjfJAKSrEyyMMmSJN9O8rBR7n9Zkm1Hs88R3HPfJCua5/pxknc35W9I8soNGYskSZIkaePQT2sE3FlVMwGSnA68CXj/2IbUkWTzqrp3VeercUlV7Z9ka2Bhkm9X1ckjabh4+QoG5sxbm5DH1DJ3OpAkSZKktdZPiYBulwO7AiTZG/g4MBG4E3h1VV2X5EnAfwIPoTNy4hDgHuC7wHxgD2Ap8MqquqPp9x1JXtj084qq+p9mW7+TgR2aOm+tqkuTHAPsCDwW+N8k1w05nwa8uaoWNnH+EHhTVV3T64Gq6vYk84HHJTkAuK2qPjoaX5YkSZIkadPRN1MDBjXb6z0HOKcp+gmwT1XtDhwNfKApfwPw8WYUQQv4ZVP+eOBTVfVE4I/AP3R1v6KqZgAnASc0ZR8Hjq+qvegkE07pqr8zsF9VHd7j/HPArCbmnYCJq0oCNHUeATyFTnJCkiRJkqSe+ikRsFWShcCvge2A85vyKcBZSZYAxwNPasovB/41yTuBR1fVnU35L6rq0ub4i8Azuu7x5a7PpzbH+wEnNfc+B9gmyeTm2jld/Q49PwvYP8kWwN8Dp63iufZJsgD4HvChqho2EZBkdpJ2kvbKO1YMV1WSJEmStAnqp0TA4BoBjwZCZ40AgGOBi6pqF+DFdKYIUFVfAg6gM8z/O0me3dSvIf3Wao43A55SVTObv2lVdVtz7fYhff35vJlucD5wIPAy4IxVPNclVbV7Ve05krUBqmpuVbWqqjVh0pTVVZckSZIkbWL6bo2AqrojyZuBbyb5FJ0RAcuby7MG6yV5LPCzqjoxyQ501hT4GbBDkqdW1eXAK4AfdnV/GPCh5vPypux7wJHAcU2/Mwfn/Y/AKcC36bzs/2GNH3Y1ZkybQtuF9yRJkiSpr/TTiIA/q6oFwCLgcOAjwAeb4fXdiZGXAUuaIf27AJ9vyq8D3pTkx8BfAJ/uavMXSRYBbwHe1pS9GWglWZTkWjprD4w0zvl01iH4zzV8REmSJEmSekrV0JHuWpUkA8C5zTSCDXG/RwIXA0+oqvtGu/9Wq1Xtdnu0u5UkSZIkjbEk86uq1etaX44I2BgkeSXwI+Bd6yMJIEmSJEnqT323RsC6qKpldKYJbIh7fZ77pyNIkiRJkjQq+nZEQJKVSRZ2/Q2sRR8HJdm56/ziJD2HXqwPSU7pvr8kSZIkSavTzyMCBrcTXBcHAecC165rMEk2r6p716RNVb12Xe65ePkKBubMW5cuxsQydzqQJEmSpLXWtyMCekmyZ5LvJ5mf5Lwk2zflr0tyVZJrknwtyaQkTwMOAI5rRhTs2HTz0iRXJrk+yT5N+wlJjmv6WJTk9U35vkkuSXIOcG1T76NJljT1jmzqPSfJgiSLk5yaZMum/M8jEJLcluT9TYxXJHqK0UYAACAASURBVNluw357kiRJkqSNQT8nArbqmhbwjSRbAJ8ADq2qPYFTgfc3db9eVXtV1W7Aj4HXVNVlwDnAP1fVzKq6oam7eVXtDbwVeHdT9hpgRVXtBewFvC7JY5prewBvqaqdgNnAADCzqnYFzkgyETgNOKyqZtAZxfHGHs+zNXBFE+MPgNf1eugks5O0k7RX3rFiTb8zSZIkSdJGzqkBjSS70FkI8PwkABOAXzWXd0nyPuBhwGTgvGH6/XrzOZ/OSz3A84BdkxzanE8BpgN3A1dW1Y1N+X7AyYNTBKrq5iS7ATdW1fVNndOBNwEnDLnv3XSmKQze+7m9gququcBcgC23n+7ekZIkSZLUZ/o5ETBUgKVV9dQe104DDqqqa5LMAvYdpp+7ms+V3P/9Bjiyqh6QQEiyL3D72of8APdU1eCLffe9JUmSJEn6M18W73cdMDXJU6vq8maqwE5VtRR4KPCrpuwIYHnT5tbm2uqcB7wxyYVVdU+Snbr66HY+8PokF1XVvUke3sQ1kORxVfU/wN8B31+nJ23MmDaFtgvvSZIkSVJf6ec1Ah6gqu4GDgU+nOQaYCHwtObyvwM/Ai4FftLV7Ezgn5uF/HZk1U6hs7PA1UmWAJ+hdxLmFOB/gUVNDK+oqj8BrwbOSrIYuA84eS0fU5IkSZLU53L/aHL1m1arVe12e6zDkCRJkiSNsiTzq6rV65ojAiRJkiRJ6iMmAiRJkiRJ6iMmAkYgyUCzW0B32T8luTbJoiQXJHn0KtpWki92nW+e5HdJzm3OD0gypzk+JslRzfFpg9sNJjklyc7r6fEkSZIkSX3EXQNWI8kbgTcDk5tkwMur6tfAAqBVVXc0dT4CHNaji9uBXZJsVVV3As+la8eAqjoHOGe4GKrqtaPyMEMsXr6CgTnz1kfX69UydzqQJEmSpLXmiIBhJHko8B46Wwb+OzCLzos9VXVRVd3RVL0CeNQwXX0HGHx7PRz4ctc9ZiU5aTVxXJyk1RwfnmRxkiVJPtxV57Yk709yTZIrkmy3Js8qSZIkSeoPJgKGdx9QwMMBqmpZVd3ao95rgP8app8zgZcnmQjsSmcrwjWW5JHAh4FnAzOBvZIc1FzeGriiqnYDfgC8bhV9zE7STtJeeceKtQlDkiRJkrQRMxEwjKq6nc4L9QeBY5N8NMmk7jpJ/hZoAccN088iYIDOaIDvrENIewEXV9Xvqupe4Azgmc21u4Fzm+P5zf16xTK3qlpV1Zowaco6hCJJkiRJ2hiZCFiNZg7/S+msATAVePvgtST7Ae8CDqiqu1bT1TnAR+maFjDK7qmqao5X4voPkiRJkqQefFkcRpLJwCOa01uBH9NME0iyO/AZ4AVV9dsRdHcqcEtVLU6y71qGdCVwYpJtgT/QGWHwibXsixnTptB24T1JkiRJ6ismAoa3BZ2X/UcA2wL/C7yiuXYcMBk4KwnA/1bVAavqqKp+CZy4LsFU1a+arQYvAgLMq6pvrUufkiRJkqT+kvtHk2tVkgwA+1bVaWMbyehqtVrVbrfHOgxJkiRJ0ihLMr+qWr2uuUbAyNwCLBzrICRJkiRJWldODRiBqjIRIEmSJEnaJJgIGKEkfwWcQGcLv1uA3wBvrarre9R9JHBiVR06TH8/Araks/jgVsDy5tJBVbVsdKPvbfHyFQzMmbchbjWqlrnAoSRJkiStNRMBI5DOaoDfAE6vqpc3ZbsB2wEPSgRU1f8Bq0wCNHWe3PQzC2hV1T+OMJbNq+reVZ1LkiRJkjQcEwEj8yzgnqo6ebCgqq5Jx3HAC4EC3ldVX2kWFzy3qnZpXvQPACYBOwLfqKp39LpJkh2BTwJTgTuA11XVT5KcBvwJ2B24NMnDh5yfCXwcmAjcCby6qq4b5e9AkiRJkrQJMBEwMrsA83uUvwSYCexGZ3vBq5L8oEe9mXRe2u8Crkvyiar6RY96c4E3VNVPkzwZ+BTw7Obao4CnVdXKJjHQfb4NsE9V3ZtkP+ADwCG9HiTJbGA2wIRtpo7g0SVJkiRJmxITAevmGcCXq2ol8Jsk36ezhsCiIfUuqKoVAEmuBR4NPCARkGQy8DTgrM5MBKCzhsCgs5r79DqfApyeZDqdkQlbrCrgqppLJ+HAlttPd+9ISZIkSeozJgJGZimrmfO/Gnd1Ha+k9/e+GXBLVc1cRR+3D3N+LHBRVR3cTEu4eO3ClCRJkiRt6kwEjMyFwAeSzG5+USfJrnR2Dzgsyel0Vv9/JvDPdObqr5Gq+mOSG5O8tKrOahYo3LWqrhlB8yncv+vArJHec8a0KbRdgV+SJEmS+spmYx3AxqCqCjgY2C/JDUmWAh8EvkRnGsA1dJIF76iqX6/DrY4AXpPkGjqjEA4cYbuPAB9MsgCTO5IkSZKkYaTzjqt+1Gq1qt1uj3UYkiRJkqRRlmR+VbV6XXNEgCRJkiRJfcREgCRJkiRJfcT55ONEkonAD+hsGbg5cHZVvTvJY4AzgUcA84G/q6q7kxwJvB74X+CgpuwZwCFV9baR3HPx8hUMzJm3Ph5nvVrmAoeSJEmStNYcETB+3AU8u6p2A2YCL0jyFODDwPFV9TjgD8BrmvpHALsClwHPb3YZ+Hc6WwlKkiRJktSTiYBxojpua063aP4KeDZwdlN+OnBQc5ymziTgHuBvgf+qqps3WNCSJEmSpI2OiYBxJMmEJAuB3wLnAzcAt1TVvU2VXwLTmuOTgCuAHYBLgVcDnxzBPWYnaSdpr7xjxWg/giRJkiRpnDMRMI5U1cqqmgk8CtgbeMIwdb9QVbtX1d8CbwNOBF6Y5Owkxyfp+b+tqrlV1aqq1oRJU9bHY0iSJEmSxjETAeNQVd0CXAQ8FXhYksFFHR8FLO+um+SRwN5V9U3g7cBhwC3AczZcxJIkSZKkjYW7BowTSaYC91TVLUm2Ap5LZ6HAi4BD6ewc8CrgW0OaHgsc3RxvRWddgfvorB0wrBnTptB2BX5JkiRJ6ismAsaP7YHTk0ygM1Ljq1V1bpJrgTOTvA9YAHxusEGS3QGq6uqm6EvAYuAXwEc2ZPCSJEmSpI1DqmqsY9AYabVa1W63xzoMSZIkSdIoSzK/qlq9rrlGgCRJkiRJfcREQJckf5XkzCQ3JJmf5DtJdhqm/m3N5yOTnN1V/uUki5K8bRRiaiU5cV37kSRJkiQJnBrwZ0kCXAacXlUnN2W7AdtU1SWraHNbVU0eUvZXwA+r6nFrcO/Nq+retY9+7Wy5/fTa/lUnbOjbrrNlLnAoSZIkScNyasDIPIvOqv0nDxZU1TXAgiQXJLk6yeIkBw5tmGQgyZLm9HvAtCQLk+yTZGaSK5oRAt9I8hdNm4uTnJCkDbylOf9wkiuTXJ9kn6bevknObY73TnJ5kgVJLkvy+KZ8VpKvJ/lukp8mcaFASZIkSVJPJgLutwswv0f5n4CDq2oPOsmC/2hGD6zKAcANVTWzGUnweeCdVbUrnRX9391V9yFV1aqq/2jON6+qvYG3Dqk36CfAPlW1O50tAz/QdW0mcBgwAzgsyf9bzfNKkiRJkvqQ2weuXoAPJHkmcB8wDdgO+PVqGyZTgIdV1febotOBs7qqfGVIk683n/OBgR5dTqGzxeB0oIAtuq5dUFUrmvteCzyazjaCQ2OaDcwGmLDN1NU9giRJkiRpE+OIgPstBfbsUX4EMBXYs6pmAr8BJo7SPW8fcn5X87mS3kmaY4GLqmoX4MVD4rir63hV7amquc0ohNaESVPWLmpJkiRJ0kbLRMD9LgS2bH4xByDJrnR+Wf9tVd2T5FnN+Yg0v9D/YXC+P/B3wPeHabI6U4DlzfGsdehHkiRJktSnnBrQqKpKcjBwQpJ30lkbYBlwDHBiksVAm848/TXxKuDkJJOAnwGvXocwP0JnasC/AfPWoR8AZkybQtsV+CVJkiSpr7h9YB9rtVrVbrfHOgxJkiRJ0ihz+0BJkiRJkgSYCJAkSZIkqa+MKBGQ5KAkleQJa3qDJPsmOXfNQ1t7SWY28b5ghPVnJXnk+o5rXSS5OEnPYR2SJEmSJI3USBcLPBz4YfP57tG4cZLNq+re0eirh+54vzuC+rOAJcD/rad4xqXFy1cwMGed1xzcpCxz8URJkiRJm7jVjghIMhl4BvAa4OVd5fs2v1KfneQnSc5IkubaC5qyq4GXdLU5JskXklwKfCHJ1CRfS3JV8/f0pt7eSS5PsiDJZUke35Q/KcmVSRYmWZRkeo94A7yUzsv9c5NMbMoHkvw4yWeTLE3yvSRbJTkUaAFnNP1uleQ5zb0XJzk1yZZNH8uSvCfJ1c21JzTlD0/yzSamK5ptB7uf9/IkP03yusEYkxyXZEnTz2Fd3+m5Xc9yUpJZPZ7x8KbdkiQfbsomJDmtq8+3re5/K0mSJEnqPyOZGnAg8N2quh74fZI9u67tDrwV2Bl4LPD05sX7s8CLgT2BvxrS387AflV1OPBx4Piq2gs4BDilqfMTYJ+q2h04GvhAU/4G4ONVNZPOy/sve8T7NODGqroBuBjo/ol3OvDJqnoScAtwSFWdTWdbwCOafgs4DTisqmbQGTXxxq4+bqqqPYBPA0c1Ze8BFlTVrsC/Ap/vqr8r8GzgqcDRzRSElwAzgd2A/YDjkmzf41kepGn/4abPmcBeSQ5qjqdV1S5N3P85kv4kSZIkSf1lJImAw4Ezm+Mzm/NBV1bVL6vqPmAhMAA8gc6L+E+rszfhF4f0d05V3dkc7weclGQhcA6wTTMCYQpwVpIlwPHAk5r6lwP/muSdwKO7+hlpvDdW1cLmeH4T71CPb+pd35yfDjyz6/rXe7R/BvAFgKq6EHhEkm2aa9+qqjur6ibgImDvpv6Xq2plVf0G+D6wV49YetkLuLiqftdMrTijie9nwGOTfKJZG+GPvRonmZ2knaS98o4VI7ylJEmSJGlTMewaAUkeTueX5xlJCpgAVJJ/bqrc1VV95er6a9zedbwZ8JSq+tOQ+54EXFRVBycZoPPLPlX1pSQ/ovMr/3eSvL558R5sN4HOyIIDk7wLCJ2X8oeuIt6tRhDvUIN9jPR5azXn3e7lgcmZiSMNqqr+kGQ34Pl0Rk68DPj7HvXmAnMBttx++nCxSJIkSZI2Qat7kT0U+EJVvX6wIMn3gX2GafMTYCDJjs3w/MOHqfs94EjguKbvmc0v9lOA5U2dWV33fizws6o6MckOdIbdX9jV33OARVX1/K42pwMHAz8YJo5bgcFkwXVN/I+rqv8B/o7OL/bDuQQ4Ajg2yb50pg/8sVky4cAkHwS2BvYF5tBJqLy+ie3hdH7R/2dgC2DnZk2CrZrn+eGQe10JnJhkW+APdL7fTzTnd1fV15Jcx4NHYjzIjGlTaLs4niRJkiT1ldVNDTgc+MaQsq8xzMt98+v+bGBes1jgb4fp/81Aq1lk71o6v2QDfAT4YJIFPDBZ8TJgSTOVYBceOBd/reJtnAac3PQb4NV0piYsBu4DTl5N+2OAPZMsAj4EvKrr2iI6UwKuAI6tqv9rYlwEXEMnkfGOqvp1Vf0C+CqdHQy+CiwYeqOq+hWdZMJFTfv5VfUtYBpwcfMMXwT+ZTUxS5IkSZL6UDrT+LU+JDkGuK2qPjrWsfTSarWq3W6PdRiSJEmSpFGWZH5VtXpdG8ligZIkSZIkaRMxksXutJaq6pixjkGSJEmSpG6OCBgDSY5P8tau8/OSnNJ1/h9J/mkVbS9O0nN4hyRJkiRJq+OIgLFxKZ2FD09IshmwLbBN1/WnAW9b30EsXr6CgTnz1vdtNirL3EVBkiRJ0ibOEQFj4zLgqc3xk+jsEnBrkr9otg58IvC8JFclWZJkbpq9CAcl2SzJaUnel2RCkuOa+ouSvB5JkiRJknowETAGmi0E702yA51f/y8HfkQnOdACFgMnVdVeVbULsBWwf1cXmwNnAD+tqn8DXgOsqKq9gL2A1yV5zAZ7IEmSJEnSRsNEwNi5jE4SYDARcHnX+aXAs5L8KMli4Nl0Rg4M+gywpKre35w/D3hlkoV0EgqPAKb3ummS2UnaSdor71ixHh5LkiRJkjSemQgYO5fSeemfQWdqwBV0RgQ8jU6S4FPAoVU1A/gsMLGr7WV0EgWDZQGOrKqZzd9jqup7vW5aVXOrqlVVrQmTpqyXB5MkSZIkjV8uFjh2LgOOAn5WVSuBm5M8jM4v/69r6tyUZDJwKHB2V9vPAc8EvprkJcB5wBuTXFhV9yTZCVheVbcPF8CMaVNouzieJEmSJPUVEwFjZzGd3QK+NKRsclXdlOSzdEYK/Bq4amjjqvpYkinAF4AjgAHg6mZRwd8BB63f8CVJkiRJG6NU1VjHoDHSarWq3W6PdRiSJEmSpFGWZH5VtXpdc40ASZIkSZL6iIkA/X/27j3crqK+//j745FbQA5yKaZBjdq0FAlG2FIFUZB6QW2xSkVUBH/WaKtStFjipQq1VryCKGqjIkFRwAtKiYIKIiAI7EBIuKmIsRJFROBACCKE7++PvSKbw8lJIOe+36/nOc9ea2bWzKzkr/3dM9+RJEmSJPUQAwGSJEmSJPWQnk8WmGQmsGdVndBV9kbgTcAqYAUwt6quHuLZZcAdTbt7h9p/keQI4L3ArKq6rik7FDgaeFpVtZN8G3hlVd2WZEVVbdbM64yq2jFJC3hNVR0yUu8NsHT5ADPnLRzJLie9ZZ6iIEmSJGmK6+kVAUn+GfgO8L4k5yZ5TFP15aqaXVVzgA8BHxumm72qas6akjA0lgKv6Lr/R+Cq1TdV9cKqum1ND1dVe6SDAJIkSZKk3tSzgYAkjwKOpHP03n8ABwN3AlTV7V1NNwXW92iFbwL7NuM+CRgAbu6ay7IkWw8z1z2TnNFcb5nkm0mWJPlxkp2a8iOSHN8ENK5PMmTgIMncJO0k7VUrB9bztSRJkiRJk03PBgKA++h8wd8SoKqWVdUdqyuTvCnJz+msCFjTr/EFfDfJoiRzhxnrduBXSXakszLglPWY95HA5VW1E/BO4MSuuu2B5wO7Au9NssGDJlw1v6paVdXqm9a/HtOQJEmSJE1GPRsIqKo7gdcDH6CzNeAjSaZ11R9XVU8CDgfevYZunllVOwP7AG9K8qxhhjyZThDgJcBp6zH1ZwJfbOZ4DrBVks2buoVVdXdV3QzcBGy7HuNIkiRJkqagnk4WWFWnJ1kC/B3QAv4NeN+gZicDn17D88ubz5uSnEbnl/jz1jDcGcCHgXZV3Z5kBN7gQe7uul7FWv5/Z8/op21yPEmSJEnqKT27IiDJZkke39zeAVwDPKqpm9XV9EXAz4Z4ftMmzwBJNgWeB1y5pvGqaiWd1QXvX8+pn08nrwFJ9gRuHpTTQJIkSZKkNerlFQEbAP8DbAVsDfwf8Mqm7s1J/ha4B7gVOAggyZ8Dn6uqF9JZdn9a88v+I+mcNHDmcANW1ckjMO8jgOOblQwrV89NkiRJkqR1kar1TYg/uSWZCexZVSeM70zGXqvVqna7Pd7TkCRJkiSNsCSL1nTMfc9uDehyG7B4vCchSZIkSdJY6PlAQFXdVlWjHghIsuIhtt8zyRnN9d8nmTc6M5MkSZIk9ZJezhEwaVTV6cDpI93v0uUDzJy3cKS7ndSWeYqCJEmSpCmu51cEjLXml/5zk3wtybVJTkqTcTDJC5qyy4CXdj1zcJJPNtd/l+TiJJcn+X6SbZvyI5Ic3/R9fZJDxuUFJUmSJEkTmoGA8fFU4FBgB+CJwO5JNgY+C/wdsAvwmDU8ewHw9Kp6KnAy8O9dddsDzwd2Bd6bZIPBDyeZm6SdpL1q5cBIvY8kSZIkaZJwa8D4uKSqbgBIshiYCawAflFVP2vKvwTMHeLZ7YBTkkwHNgR+0VW3sKruBu5OchOdIw5v6H64quYD8wE2mj6rt4+MkCRJkqQe5IqA8XF31/UqHlpA5hPAJ6tqNvAGYOMR6leSJEmS1AP8ojhxXAvMTPKkqvo5cMAa2vUDy5vrg9ZnwNkz+mmbHE+SJEmSeoorAiaIqvoDna0AC5tkgTetoekRwFeTLAJuHqPpSZIkSZKmiFS5TbxXtVqtarfb4z0NSZIkSdIIS7KoqlpD1bkiQJIkSZKkHmIgQJIkSZKkHmIgYIpI8ldJFnf93Z7k0PGelyRJkiRpYvHUgCmiqn4CzAFI0kfnZIHThntm6fIBZs5bOAazmzyWeYqCJEmSpCnOFQFT097Az6vql+M9EUmSJEnSxGIgYGp6BfCVoSqSzE3STtJetXJgjKclSZIkSRpvBgKmmCQbAn8PfHWo+qqaX1Wtqmr1Tesf28lJkiRJksadgYCpZx/gsqr67XhPRJIkSZI08ZgscOo5gDVsCxhs9ox+2ibHkyRJkqSe4oqAKSTJpsBzgW+M91wkSZIkSROTKwKmkKq6E9hqvOchSZIkSZq4XBEgSZIkSVIPMRAgSZIkSVIPcWvABJNkRVVt1nV/MNCqqjcP88yf2iTZBjgD2BA4pKrOX9NzS5cPMHPewhGb+1SwzOSJkiRJkqY4AwFTz97A0qr6p/GeiCRJkiRp4nFrwCSS5O+SXJzk8iTfT7LtoPo5wIeAfZMsTrLJ+MxUkiRJkjRRuSJg4tkkyeKu+y2B05vrC4CnV1Ul+Sfg34F/W92wqhYneQ/DbCVIMheYC9C3+TajMX9JkiRJ0gRmIGDiuauq5qy+Wb3/v7ndDjglyXQ6OQB+8VA7r6r5wHyAjabPqvWerSRJkiRpUnFrwOTyCeCTVTUbeAOw8TjPR5IkSZI0ybgiYHLpB5Y31wetb2ezZ/TTNku+JEmSJPUUVwRMLkcAX02yCLh5nOciSZIkSZqEUuU28V7VarWq3W6P9zQkSZIkSSMsyaKqag1V54oASZIkSZJ6iIEASZIkSZJ6iMkCx1GSxwInAtsCBcyvqo8n2RI4BZgJLANeXlW3Jtke+AKwM/CuqvpIV1/LgDuAVcC9a1oC0m3p8gFmzls4ou802S0zeaIkSZKkKc4VAePrXuDfqmoH4OnAm5LsAMwDzq6qWcDZzT3ALcAhwEeG6gzYq6rmrEsQQJIkSZLUmwwEjKOq+k1VXdZc3wFcA8wA9gUWNM0WAC9p2txUVZcC94zDdCVJkiRJU4CBgAkiyUzgqcDFwLZV9Zum6kY6WwfWpoDvJlmUZO4w48xN0k7SXrVyYD1nLUmSJEmabMwRMAEk2Qz4OnBoVd2e5E91VVVJ1uWMx2dW1fIkfwZ8L8m1VXXe4EZVNR+YD7DR9FmeHSlJkiRJPcYVAeMsyQZ0ggAnVdU3muLfJpne1E8HblpbP1W1vPm8CTgN2HV0ZixJkiRJmsxcETCO0vnp//PANVX1sa6q04GDgKOaz2+tpZ9NgUdU1R3N9fOA/1zb+LNn9NM2S74kSZIk9RQDAeNrd+BAYGmSxU3ZO+kEAE5N8jrgl8DLAZI8BmgDmwP3JTkU2AHYGjit2VLwSODLVXXmWL6IJEmSJGlyMBAwjqrqAiBrqN57iPY3AtsN0fZ24CkjODVJkiRJ0hRljgBJkiRJknqIgYAJKMmqJIu7/mYmuXC85yVJkiRJmvzcGjAx3VVVcwaV7TbSgyxdPsDMeQtHuttJbZnJEyVJkiRNca4ImCSSrGg+T07yoq7yE5Lsl6QvyYeTXJpkSZI3jN9sJUmSJEkTlYGAiWmTrm0Bpw2qO4X7TxHYkE5SwYXA64CBqnoa8DTg9UmeMJaTliRJkiRNfG4NmJiG2hqw2neAjyfZCHgBcF5V3ZXkecBOSfZr2vUDs4BfdD+cZC4wF6Bv821GZfKSJEmSpInLQMAkU1V/SHIu8Hxgf+DkpirAW6rqrLU8Px+YD7DR9Fk1ilOVJEmSJE1Abg2YnE4BXgvsAZzZlJ0F/HOSDQCS/GWSTcdpfpIkSZKkCcoVAZPTd4EvAt+qqj82ZZ8DZgKXJQnwO+Alw3Uye0Y/bbPkS5IkSVJPMRAwAVXVZsOVVdU9wJaD6u8D3tn8SZIkSZI0JLcGSJIkSZLUQwwESJIkSZLUQwwEjKMkj03ygyRXJ7kqyb825Vsm+V6SnzWfj27KX5VkSZKlSS5M8pSuvl6Q5CdJrksyb7zeSZIkSZI0saXKE+TGS5LpwPSquizJo4BFdBL8HQzcUlVHNV/qH11VhyfZDbimqm5Nsg9wRFX9TZI+4KfAc4EbgEuBA6rq6uHG32j6rJp+0DGj94KT0DKTJ0qSJEmaApIsqqrWUHWuCBhHVfWbqrqsub4DuAaYAewLLGiaLaDJ/l9VF1bVrU35j4Htmutdgeuq6vrmFIGTmz4kSZIkSXoAAwETRJKZwFOBi4Ftq+o3TdWNwLZDPPI64DvN9QzgV111NzRlkiRJkiQ9gMcHTgBJNgO+DhxaVbcn+VNdVVWSGtR+LzqBgGc+jLHmAnMB+jbfZn2mLUmSJEmahFwRMM6SbEAnCHBSVX2jKf5tkz9gdR6Bm7ra7wR8Dti3qn7fFC8HHtvV7XZN2YNU1fyqalVVq29a/8i+jCRJkiRpwnNFwDhK56f/z9NJAPixrqrTgYOAo5rPbzXtHwd8Aziwqn7a1f5SYFaSJ9AJALwCeOXaxp89o5+2yfEkSZIkqacYCBhfuwMHAkuTLG7K3kknAHBqktcBvwRe3tS9B9gK+FSzfeDe5tf9e5O8GTgL6AOOr6qrxvA9JEmSJEmThMcH9rBWq1Xtdnu8pyFJkiRJGmEeHyhJkiRJkgADAZIkSZIk9RQDAWMoyYpB9wcn+WRzfUSSSvIXXfWHNmWt5n5ZkqVJliT5YZLHd7V9TZIrm/rLkxw2Vu8lSZIkSZo8TBY4sSylk/H/v5r7fwQGJ/3bq6puTnIk8G7g9Un2AQ4FnldVv06yEfCatQ62fICZ8xaO3OyngGWeoiBJkiRpinNFwMTyTWBfgCRPAgaAm9fQ9iJgRnP9DuCwqvo1QFXdXVWfHeW5SpIkSZImIVcEjK1Nuo4JBNgSOL3r/nbgV0l2pBMQWJC3ywAAIABJREFUOAV47Rr6egGdwAHAjsCiEZ6rJEmSJGkKMhAwtu6qqjmrb5IcDAw+zuFkOtsDng/szYMDAT9IsiWwAviPhzqBJHOBuQB9m2/zUB+XJEmSJE1ybg2YeM4ADgT+r6puH6J+L+DxwGLgyKbsKmCXdem8quZXVauqWn3T+kdivpIkSZKkScQVARNMVa1Mcjjw02Ha3JvkUGBpkv8CPgB8OMmLqurGJBsCr6mqzw031uwZ/bRNjidJkiRJPcVAwARUVSevQ5vfJPkK8Kaqel+SbYHvJwlQwPGjPU9JkiRJ0uSTqhrvOWictFqtarfb4z0NSZIkSdIIS7KoqgbnpAPMESBJkiRJUk8xECBJkiRJUg8xEDDCklSSj3bdH5bkiOb6jUleM4JjfTvJFiPVnyRJkiRp6jNZ4Mi7G3hpkg9U1c3dFVX1mZEcqKpeuD7PL10+wMx5C0dqOlPCMk9RkCRJkjTFuSJg5N0LzAfeOrgiyRFJDmuu/yLJ95NckeSyJE9Kx4eTXJlkaZL9m7bTk5yXZHFTt0dTvizJ1s31N5MsSnJVkrlj97qSJEmSpMnEFQGj4zhgSZIPDdPmJOCoqjotycZ0gjIvBeYATwG2Bi5Nch7wSuCsqnp/kj5g2hD9/b+quiXJJs1zX6+q3w9u1AQJ5gL0bb7NeryiJEmSJGkyMhAwCqrq9iQnAocAdw2uT/IoYEZVnda0/0NT/kzgK1W1Cvhtkh8CTwMuBY5PsgHwzapaPMSwhyT5h+b6scAs4EGBgKqaT2fFAhtNn+XZkZIkSZLUY9waMHqOAV4HbLq+HVXVecCzgOXACYMTDibZE/hb4BlV9RTgcmDj9R1XkiRJkjT1uCJglDTL9E+lEww4flDdHUluSPKSqvpmko2APuB84A1JFgBb0vny//YkjwduqKrPNm13Bk7s6rIfuLWqVibZHnj6usxx9ox+2ibHkyRJkqSe4oqA0fVROnv9h3IgneX8S4ALgccApwFLgCuAc4B/r6obgT2BK5JcDuwPfHxQX2cCj0xyDXAU8OMRfg9JkiRJ0hSRKreJ96pWq1Xtdnu8pyFJkiRJGmFJFlVVa6g6VwRIkiRJktRDDARIkiRJktRDDASMsiSrkixOcmWS/02yxQj2/bkkO4xUf5IkSZKkqc8cAaMsyYqq2qy5XgD8tKreP87TAmCj6bNq+kHHjPc0JpRlnqIgSZIkaQowR8DEcREwAyDJuUlazfXWSZY1109OckmzimBJkllJNk2yMMkVzcqC/Yfo49NJ2kmuSnLk+LyeJEmSJGmie+R4T6BXJOkD9gY+v5ambwQ+XlUnJdkQ6ANeCPy6ql7U9NU/xHPvqqpbmnHOTrJTVS0ZYh5zgbkAfZtv8/BfSJIkSZI0KbkiYPRtkmQxcCOwLfC9tbS/CHhnksOBx1fVXcBS4LlJPphkj6oaGOK5lye5DLgceDIwZO6AqppfVa2qavVNGyqeIEmSJEmaygwEjL67qmoO8HggwJua8nu5/99/49WNq+rLwN8DdwHfTvKcqvopsDOdgMB/JXlP9wBJngAcBuxdVTsBC7v7lCRJkiRpNbcGjJGqWpnkEOCbST4FLAN2AS4B9lvdLskTgeur6tgkjwN2SnItcEtVfSnJbcA/Dep+c+BOYCDJtsA+wLlrm9PsGf20TY4nSZIkST3FQMAYqqrLkywBDgA+Apza7Nlf2NXs5cCBSe6hs53gv4GnAR9Och9wD/DPg/q9IsnlwLXAr4AfjfrLSJIkSZImJY8P7GGtVqva7fZ4T0OSJEmSNMI8PlCSJEmSJAEGAiRJkiRJ6imTIkdAkwDvaODpwK3AH4EPVdVpD6OvFvCaqjrkIT43h87RfPtU1ZkP8dmZwBlVteNDeW60LV0+wMx5C9fesIcsM3miJEmSpCluwq8ISBLgm8B5VfXEqtoFeAWw3cPpr6raDzUI0DgAuKD5HBFJJkUgRpIkSZI0dUz4QADwHOCPVfWZ1QVV9cuq+gR0fm1Pcn6Sy5q/3Zryk5P86efdJCck2S/JnknOaMqOSHJ8knOTXN8c7/cgTTDiH4GDgecm2bhr7GuSfDbJVUm+m2STpm6XJFckuQJ4U1dfByc5Pck5wNlJNm3mcEmSy5Ps27R7clO2OMmSJLOa8ld3lf9Pkr6mfEWS9zdj/rhZRSFJkiRJ0gNMhkDAk4HLhqm/CXhuVe0M7A8c25SfQucoPpJsCOzNA4/pW2174PnArsB7k2wwRJvdgF9U1c+Bc4Hu9eOzgOOq6snAbcDLmvIvAG+pqqcM0d/OwH5V9WzgXcA5VbUrsBedYwI3Bd4IfLyq5gAt4IYkf9284+5N+SrgVU2fmwI/bsY7D3j9UP9YSeYmaSdpr1o5MFQTSZIkSdIUNhkCAQ+Q5LjmV+9Lm6INgM8mWQp8FdihKf8OsFeSjYB96GwtuGuILhdW1d1VdTOdoMJQv6QfAJzcXJ/MA7cH/KKqFjfXi4CZSbYAtqiq85ryLw7q73tVdUtz/TxgXpLFdIIMGwOPAy4C3pnkcODxzdz3BnYBLm3a7w08sennj8AZ3fMY4j2oqvlV1aqqVt+0/qGaSJIkSZKmsMmwR/0q7v+Vnap6U5KtgXZT9Fbgt8BT6AQ2/tC0+0OSc+n82r8/93+RH+zurutVDPo3aZbevwzYN8m7gABbJXnUGp7fZB3e6c7uIYCXVdVPBrW5JsnFdFYffDvJG5q2C6rqHUP0eU9V1ZreQ5IkSZIkmBxfFs8B/jvJP1fVp5uyaV31/cANVXVfkoOAvq66U4B/orO0/uCHOf7ewJKqev7qgiQLgH+gswT/QarqtiS3JXlmVV3A/cv3h3IW8JYkb6mqSvLUqro8yROB66vq2CSPA3YCvgt8K8nRVXVTki2BR1XVLx/Oi82e0U/bLPmSJEmS1FMm/NaA5lfulwDPTvKLJJcAC4DDmyafAg5qkvJtzwN/bf8u8Gzg+1X1x4c5hQOAwccUfp21nx7wWuC4Zgl/hmn3PjrbG5Ykuaq5h05+gyub53cETqyqq4F3A99NsgT4HjD9obyMJEmSJKm35f7V5Oo1rVar2u322htKkiRJkiaVJIuqqjVU3YRfESBJkiRJkkaOgQBJkiRJknrIZEgWOGElKeBjVfVvzf1hwGZVdURXm8XAtVX1iq6ypwMfBzZq/k5Z/UySfejkCZhG50SCc1b3P0x/J9DJhTBAJx/B26rq7LXNf+nyAWbOW/iw3n2qWmbyREmSJElTnCsC1s/dwEub4wwfJMlf0znFYI8km3ZVLQDmVtUcOokAT23a7wh8Enh1Ve1A57SD69ahP4C3N/0dCnxmJF5OkiRJkjT1GAhYP/cC84G3rqH+AOCLdE4v2Ler/M+A3wBU1armNACAfwfeX1XXdtV9eh3663YRMOOhv4okSZIkqRcYCFh/xwGvStI/RN3+wMnAV3jgcYNHAz9JclqSNyTZuCnfEVg0zFhr6q/bC4BvrqmDJHOTtJO0V60cGGYoSZIkSdJUZCBgPVXV7cCJwCHd5UlawM1V9X/A2cBTk2zZPPOfdJb9fxd4JXDm2sYZrr/Gh5P8FPgy8MFh5ju/qlpV1eqbNlTsQpIkSZI0lRkIGBnHAK8DuvftHwBsn2QZ8HNgc+Blqyur6ufNsv+9gack2Qq4CthlDWMM2x+dHAF/CRwOHD8C7yRJkiRJmoI8NWAEVNUtSU6lEww4PskjgJcDs6vq1wBJ9gL+A/hskhcB366qAmYBq4DbgA8D30hyQVX9tOlnLp08BGvsb9B0Pgn8vyTPr6qzhpv37Bn9tM2SL0mSJEk9xRUBI+ejwOrTA/YAlq/+0t44D9ghyXTgQDo5AhbTSf73qiYx4BI6Wf+/kuQa4ErgievQ3580wYX/opN4UJIkSZKkB0jne6N6UavVqna7Pd7TkCRJkiSNsCSLqqo1VJ0rAiRJkiRJ6iEGAiRJkiRJ6iEmC5zAkvQBbTr5AV6c5AnAycBWwCLgwKr6Y5KN6BxhuAvwe2D/qlq2tv6XLh9g5ryFozb/yWiZyRMlSZIkTXGuCJjY/hW4puv+g8DRVfUXwK10Timg+by1KT+6aSdJkiRJ0oMYCJigkmwHvAj4XHMf4DnA15omC4CXNNf7Nvc09Xs37SVJkiRJegADARPXMXSOALyvud8KuK2q7m3ubwBmNNczgF8BNPUDTfsHSTI3STtJe9XKgdGauyRJkiRpgjIQMAEleTFwU1UtGum+q2p+VbWqqtU3rX+ku5ckSZIkTXAmC5yYdgf+PskLgY2BzYGPA1skeWTzq/92wPKm/XLgscANSR4J9NNJGihJkiRJ0gMYCJiAquodwDsAkuwJHFZVr0ryVWA/OicHHAR8q3nk9Ob+oqb+nKqqtY0ze0Y/bbPkS5IkSVJPcWvA5HI48LYk19HJAfD5pvzzwFZN+duAeeM0P0mSJEnSBOeKgAmuqs4Fzm2urwd2HaLNH4B/HNOJSZIkSZImJVcESJIkSZLUQwwETCJJViVZ3PU3rylflmTrrnZ7Jjlj/GYqSZIkSZqo3BowudxVVXNGqrOlyweYOW/hSHU3JSwzeaIkSZKkKc4VAZIkSZIk9RADAZPLJoO2Buw/3hOSJEmSJE0ubg2YXNa0NaDWsYwkc4G5AH2bbzOCU5MkSZIkTQauCJgafg88uut+S+DmoRpW1fyqalVVq29a/5hMTpIkSZI0cRgImBrOBQ4ESNIHvBr4wXhOSJIkSZI0Mbk1YHLZJMnirvszq2oe8D7g00muAAKcCXxpbZ3NntFP2yz5kiRJktRTDARMIlXVt4byAeCVYzwdSZIkSdIk5NYASZIkSZJ6iIEASZIkSZJ6iIGACS7JxkkuSXJFkquSHLkOz+yZ5IyxmJ8kSZIkaXIxR8DEdzfwnKpakWQD4IIk36mqH69ukKSvqlY91I6XLh9g5ryFIznXSW+ZyRMlSZIkTXEGAia4qipgRXO7QfNXSZYBpwDPBT6U5DbgGGAlcME4TFWSJEmSNAkYCJgEkvQBi4C/AI6rqouTAPy+qnZOsjHwM+A5wHV0AgSSJEmSJD2IOQImgapaVVVzgO2AXZPs2FSt/sK/PfCLqvpZs4LgS2vqK8ncJO0k7VUrB0Z34pIkSZKkCcdAwCRSVbcBPwBe0BTd+TD6mF9Vrapq9U3rH9H5SZIkSZImPrcGTHBJtgHuqarbkmxCJyfABwc1uxaYmeRJVfVz4IB16Xv2jH7aJseTJEmSpJ7iioCJbzrwgyRLgEuB71XVA44GrKo/AHOBhUkuA24a+2lKkiRJkiYDVwRMcFW1BHjqEOUzB92fSSdXgCRJkiRJa+SKAEmSJEmSeoiBAEmSJEmSeoiBgAkiyWOT/CDJ1UmuSvKvTfmWSb6X5GfN56Ob8pc17c5PslVT9qQkpww3jiRJkiSpt6Vz7LzGW5LpwPSquizJo4BFwEuAg4FbquqoJPOAR1fV4UnOBV4IvLQp+0SSrwDvqaqfrcuYG02fVdMPOmY0XmfSWuYpCpIkSZKmgCSLqqo1VJ0rAiaIqvpNVV3WXN8BXAPMAPYFFjTNFtAJDgDcB2wETAPuSbIHcOO6BgEkSZIkSb3JUwMmoCQz6ZwUcDGwbVX9pqm6Edi2uf4A8H3g18Crga8CrxjTiUqSJEmSJh1XBEwwSTYDvg4cWlW3d9dVZx9HNdffq6pdqurv6Kwa+Dbwl0m+luSzSaatof+5SdpJ2qtWDozuy0iSJEmSJhwDARNIkg3oBAFOqqpvNMW/bfIHrM4jcNOgZ6bRySNwHHAkcBBwAfCqocaoqvlV1aqqVt+0/lF5D0mSJEnSxOXWgAkiSYDPA9dU1ce6qk6n8+X+qObzW4MefTtwbFXdk2QTOisG7qOTO2BYs2f00zY5niRJkiT1FAMBE8fuwIHA0iSLm7J30gkAnJrkdcAvgZevfiDJnwO7VtWRTdEngEuB27g/qaAkSZIkSX9iIGCCqKoLgKyheu81PPNr4EVd91+lkzRQkiRJkqQhmSNAkiRJkqQeYiBAkiRJkqQe0pOBgCRHJzm06/6sJJ/ruv9okrc9hP6OSHLYGuouXI957plkt4f7vCRJkiRJg/VqjoAf0Um6d0ySRwBbA5t31e8GvHUkBqqq9fkivyewAnjYwYThLF0+wMx5C0ej60lrmacoSJIkSZrienJFAJ0v1s9orp8MXAnckeTRSTYC/hp4XpJLk1yZZH5zvB9JDklydZIlSU7u6nOHJOcmuT7JIasLk6xoPvds6r+W5NokJ3X1+cKmbFGSY5OckWQm8EbgrUkWJ9kjycwk5zRjn53kcc3zJzTPXdiMv9+o/utJkiRJkiatngwENNn2722+SO8GXARcTCc40AKWAp+sqqdV1Y7AJsCLm8fnAU+tqp3ofFFfbXvg+cCuwHuTbDDE0E8FDgV2AJ4I7J5kY+B/gH2qahdgm2aOy4DPAEdX1ZyqOp/O8YALmrFPAo7t6ns68Mxmnket6d2TzE3STtJetXJg7f9YkiRJkqQppScDAY0L6QQBVgcCLuq6/xGwV5KLkywFnkNn5QDAEuCkJK8G7u3qb2FV3V1VNwM3AdsOMeYlVXVDVd0HLAZm0gkgXF9Vv2jafGWYOT8D+HJz/UU6X/xX+2ZV3VdVV69hbACqan5Vtaqq1Tetf5ihJEmSJElTUS8HAn5E50v/bDpbA35M54v2bnSCBJ8C9quq2cBngY2b514EHAfsDFyaZHWehbu7+l7F0PkX1qXNw9Xdd0awX0mSJEnSFNKryQKh82X/MDq/xq8CbkmyBZ1f/l/ftLk5yWbAfsDXmsSCj62qHyS5AHgFsNl6zuMnwBOTzGy2A+zfVXcHD0xieGEz5heBVwHnr8/As2f00zY5niRJkiT1lF4OBCylc1rAlweVbVZVNyf5LJ2VAjcClzb1fcCXkvTT+dX92Kq6rcn597BU1V1J/gU4M8mdXWMB/C+dAMS+wFuavy8keTvwO+C1D3tgSZIkSVJPSlWN9xx6XpLNqmpFc4rAccDPquro0R631WpVu90e7WEkSZIkSWMsyaKqag1V18s5AiaS1ydZDFwF9NM5RUCSJEmSpBHXy1sDJozm1/9RXwEgSZIkSZIrAhpJVgy6PzjJJ0d5zBOS7Ndcb5nk8iTu+5ckSZIkjRpXBEwATfLBs4D5VfWFsRp36fIBZs5bOFbDTQrLPEVBkiRJ0hTnioB1kGRmknOSLElydpLHNeUnJDk2yYVJru/6df8RST6V5Nok30vy7dV1Q9gM+A7w5ar6dPP8nCQ/bsY7Lcmjm/Jzk3wwySVJfppkj6Z8WpJTk1zdtL84yZBJISRJkiRJvc1AwP02SbJ49R/wn111nwAWVNVOwEnAsV1104FnAi8GjmrKXgrMBHYADgSeMcy4HwMuGHRKwInA4c14S4H3dtU9sqp2BQ7tKv8X4Naq2gH4D2CXNQ2WZG6SdpL2qpUDw0xLkiRJkjQVGQi4311VNWf1H/CerrpnAF9urr9I54v/at+sqvuq6mpg26bsmcBXm/IbgR8MM+45wL5J/gz+tE1gi6r6YVO/AHhWV/tvNJ+L6AQbVo93MkBVXQksWdNgVTW/qlpV1eqb1j/MtCRJkiRJU5GBgPV3d9d1HsbzJwOfAb6d5FEPYbxVmONBkiRJkvQQ+UVy3VwIvILOaoBXAeevpf2PgIOSLAC2Afbk/hUFD1JVRyd5DJ1f+18E3Jpkj6o6n87Wgh+u6dmu8V4O/CDJDsDstb4RMHtGP22T40mSJElSTzEQsG7eAnwhyduB3wFrO+Lv68DewNXAr4DLgGE35FfV4Um+QCfY8FrgU0mmAdevw3ifAhYkuRq4FrhqbeNJkiRJknpTqmq85zAlJdmsqlYk2Qq4BNi9yRcwGmP1ARtU1R+SPAn4PvBXVfXH4Z5rtVrVbrdHY0qSJEmSpHGUZFFVDXmanCsCRs8ZSbYANgTeN1pBgMY0OtsCNqCTp+Bf1hYEkCRJkiT1JgMBo6Sq9hzDse4Ahoz0SJIkSZLUzUDAOEjyLuCVdDL/3we8oaouXssz/wmcV1XfH6l5LF0+wMx5C0equylhmckTJUmSJE1xBgLGWJJnAC8Gdq6qu5NsTWf7wLCq6j2jPjlJkiRJ0pT3iPGeQA+aDtxcVXcDVNXNwIwk3wBIsm+Su5JsmGTjJNc35Sck2a+5PirJ1UmWJPlIU/aPSa5MckWS88bn1SRJkiRJE50rAsbed4H3JPkpnez+pwA/AuY09XsAVwJPo/P/84AtA80pBP8AbF9V1SQkBHgP8PyqWt5V9iBJ5gJzAfo232bEXkqSJEmSNDm4ImCMVdUKYBc6X8Z/RycQ8Grg50n+GtgV+BjwLDpBgfMHdTEA/AH4fJKXAiub8h8BJyR5PdA3zPjzq6pVVa2+af0j92KSJEmSpEnBQMA4qKpVVXVuVb0XeDPwMuA8YB/gHjorBZ7Z/J0/6Nl76QQLvkYn18CZTfkbgXcDjwUWNSsHJEmSJEl6ALcGjLEkfwXcV1U/a4rmAL+k84X/RODEqvpd80V+WzrbBLqf3wyYVlXfTvIjYHUOgSc1Jw9cnGQfOgGB3w83l9kz+mmbJV+SJEmSeoqBgLG3GfCJZh//vcB1dLYJ3Enni//qRH9LgMdUVQ16/lHAt5JsDAR4W1P+4SSzmrKzgStG9S0kSZIkSZNSHvw9U72i1WpVu90e72lIkiRJkkZYkkVV1RqqzhwBkiRJkiT1EAMBkiRJkiT1kJ7PEZDkMcAxwNOA24DfAocCfw4cVlUvXo++jwBWVNVHkvwncF5Vff+hPvtwx1+bpcsHmDlv4Wh1PyktM3miJEmSpCmupwMBSQKcBiyoqlc0ZU+hk7Rvfft+wL9tVb1nffuUJEmSJGl99frWgL2Ae6rqM6sLquqKqjq/ud0sydeSXJvkpCZwQJL3JLk0yZVJ5neVn5vkmCRt4F+7B0pyQpL9mutdkvwwyaIkZyWZvq4TTvL2ZuwlSY7sKn91kkuSLE7yP0n6Hva/iiRJkiRpyur1QMCOwKJh6p9KZ5vADsATgd2b8k9W1dOqakdgE6B7+8CGVdWqqo8O1WGSDYBPAPtV1S7A8cD712WySZ4HzAJ2BeYAuyR5VpK/BvYHdq+qOcAq4FVr6GNuknaS9qqVA+syrCRJkiRpCunprQHr4JKqugEgyWJgJnABsFeSfwemAVsCVwH/2zxzylr6/Cs6AYjvNQsJ+oDfrON8ntf8Xd7cb0YnMLATsAtwadPnJsBNQ3VQVfOB+QAbTZ/l2ZGSJEmS1GN6PRBwFbDfMPV3d12vAh6ZZGPgU0Crqn7VJPXbuKvdnWsZM8BVVfWMhzHfAB+oqv95QGHyFjp5Dt7xMPqUJEmSJPWQXg8EnAP8d5K5zS/lJNkJ6B/mmdVf+m9OshmdQMLXHsKYPwG2SfKMqrqo2Srwl1V11To8exbwviQnVdWKJDOAe4CzgW8lObqqbkqyJfCoqvrlcJ3NntFP2yz5kiRJktRTejoQUFWV5B+AY5IcDvwBWEYnL8CMNTxzW5LPAlcCNwKXPsQx/9gkDTw2ST+d/4Nj6KxOGOzdSQ7tena7Jh/ARc0WgBXAq6vq6iTvBr6b5BF0ggNvAoYNBEiSJEmSek+q3Cbeq1qtVrXb7fGehiRJkiRphCVZVFWtoep6/dQASZIkSZJ6ioEASZIkSZJ6SE/nCBhNSY4HXgzcVFU7NmVb0jlecCadXAQvr6pbh3j2HcDr6JxUcEhVndWUvwD4OJ0jBz9XVUc15U8ATga2AhYBB1bVH9c2x6XLB5g5b+H6vegUs8zkiZIkSZKmOFcEjJ4TgBcMKpsHnF1Vs+hk+p83+KEkOwCvAJ7cPP+pJH1J+oDjgH2AHYADmrYAHwSOrqq/AG6lE0SQJEmSJOlBDASMkqo6D7hlUPG+wILmegHwkiEe3Rc4uarurqpfANcBuzZ/11XV9c2v/ScD+6ZzfMBzuP8IwzX1K0mSJEmSgYAxtm1V/aa5vhHYdog2M4Bfdd3f0JStqXwr4LaqundQ+ZCSzE3STtJetXLg4b2FJEmSJGnSMhAwTqpzbuOYn91YVfOrqlVVrb5p/WM9vCRJkiRpnBkIGFu/TTIdoPm8aYg2y4HHdt1v15Stqfz3wBZJHjmoXJIkSZKkB/HUgLF1OnAQcFTz+S2AJLsCb66q1zRtvpzkY8CfA7OAS4AAs5oTApbTSSj4yqqqJD8A9qOTN+BP/a7N7Bn9tM2SL0mSJEk9xRUBoyTJV4CLgL9KckOS19EJADw3yc+Av23uAR4H3AVQVVcBpwJXA2cCb6qqVU0OgDcDZwHXAKc2bQEOB96W5Do6OQM+PxbvKEmSJEmafNLZqq7xlOTDwBeraslYjttqtardbo/lkJIkSZKkMZBkUVW1hqpza8AEUFVvH+85SJIkSZJ6g1sDRkGSVUkWd/3NTHLhOjx3bpIhIzYPYw7Lkmw9En1JkiRJkqYOVwSMjruqas6gst3GZSbDWLp8gJnzFo73NCaUZSZPlCRJkjTFuSJgjCRZ0Xzu2fzy/7Uk1yY5KUmGaP/pJO0kVyU5sqt8WZIjk1yWZGmS7ZvyrZJ8t2n/OTqnDEiSJEmS9AAGAkbHJl3bAk4bov6pwKHADsATgd2HaPOuJrHDTsCzk+zUVXdzVe0MfBo4rCl7L3BBVT0ZOI3OSQSSJEmSJD2AgYDRcVdVzWn+/mGI+kuq6oaqug9YDMwcos3Lk1wGXA48mU7QYLVvNJ+Lup59FvAlgKpaCNw61MSSzG1WGrRXrRx4iK8lSZIkSZrsDASMj7u7rlcxKFdDkifQ+aV/76raCVgIbDzE8w96dm2qan5Vtaqq1Tet/yFPXJIkSZI0uRkImJg2B+4EBpJsC+yzDs+cB7wSIMk+wKNHb3qSJEmSpMnKUwMmoKq6IsnlwLXAr4AfrcNjRwJfSXIVcCHwf2t7YPaMftr5zqiXAAAgAElEQVRmyZckSZKknpKqGu85aJy0Wq1qt9vjPQ1JkiRJ0ghLsqhJQP8gbg2QJEmSJKmHGAiQJEmSJKmHGAgYQUkqyUe77g9LcsQ4TkmSJEmSpAcwWeDIuht4aZIPVNXND/XhJI+sqntHYV5DWrp8gJnzFo7VcJPCMpMnSpIkSZriXBEwsu4F5gNvHVyRZGaSc5IsSXJ2ksc15Sck+UySi4EPJVmaZIt0/D7Ja5p2JyZ5btPP+Ukua/5266p/Sdd4JyXZd0zeWpIkSZI0aRgIGHnHAa9K0j+o/BPAgqraCTgJOLarbjtgt6p6G52jAncHngxcD+zRtHkGnWMBbwKeW1U7A/t39fN54GCAZuzdAH/ulyRJkiQ9gIGAEVZVtwMnAocMqnoG8OXm+ovAM7vqvlpVq5rr84FnNX+fBmYnmQHcWlV3AhsAn02yFPgqsEMz7g+BWUm2AQ4Avj7UNoMkc5O0k7RXrRxY/xeWJEmSJE0qBgJGxzHA64BN17H9nV3X59FZBbAHcC7wO2A/OgEC6Gw7+C3wFKAFbNj17InAq4HXAscPNVBVza+qVlW1+qYNXrQgSZIkSZrqTBY4CqrqliSn0gkGrP5CfiHwCjqrAV7F/V/sBz/7qyRbAxtW1fVJLgAOA97cNOkHbqiq+5IcBPR1PX4CcAlwY1VdvbZ5zp7RT9vkeJIkSZLUU1wRMHo+Cmzddf8W4LVJlgAHAv86zLMXAz9trs8HZgAXNPefAg5KcgWwPV2rCarqt8A1wBdG4gUkSZIkSVNPqmq856ARkmQasBTYuarWmgCg1WpVu90e/YlJkiRJksZUkkVV1RqqzhUBU0SSv6WzGuAT6xIEkCRJkiT1JnMETBFV9X3g8eM9D0mSJEnSxOaKgAkqyfFJbkpyZVfZlkm+l+Rnzeejm/IkOTbJdUmWJNl5/GYuSZIkSZrIzBEwQSV5FrACOLGqdmzKPgTcUlVHJZkHPLqqDk/yQjrJCF8I/A3w8ar6m7WNsdH0WTX9oGNG7yUmoWWeoiBJkiRpCjBHwCRUVecBtwwq3hdY0FwvAF7SVX5idfwY2CLJ9LGZqSRJkiRpMjEQMLlsW1W/aa5vBLZtrmcAv+pqd0NTJkmSJEnSAxgImKSqs6fjIe/rSDI3STtJe9VKDxeQJEmSpF5jIGBy+e3qJf/N501N+XLgsV3ttmvKHqSq5ldVq6pafdP6R3WykiRJkqSJx+MDJ5fTgYOAo5rPb3WVvznJyXSSBQ50bSFYo9kz+mmbHE+SJEmSeoqBgAkqyVeAPYGtk9wAvJdOAODUJK8Dfgm8vGn+bTonBlwHrAReO+YTliRJkiRNCgYCJqiqOmANVXsP0baAN43ujCRJkiRJU4E5AiRJkiRJ6iEGAiRJkiRJ6iEGAkZIklVJFie5Msn/JtliBPv+XJIdRqo/SZIkSVLvSmd7udZXkhVVtVlzvQD4aVW9f5ynNayNps+q6QcdM97T6AnLPJ1BkiRJ0hhKsqiqWkPVuSJgdFwEzABIcm6SVnO9dZJlzfWTk1zSrCJYkmRWkk2TLExyRbOyYP8h+vh0knaSq5IcuXrAJMuSHJnksiRLk2w/1i8tSZIkSZr4DASMsCR9dDL7n76Wpm8EPl5Vc4AWcAPwAuDXVfWUqtoROHOI597VRHV2Ap6d5P+zd+9xdlX1/f9fb0cqicgIQhGiMtZiqRCIcKDeQEGt2lpFpSLewFqiflW0La3R+hPUVvBSL3htRAUUFUXxFm+Ui4AgcCJJJgjVorEaBOSWCkGE4fP74+zRwzCTmSSTmTMzr+fjMY+zz2etvfZnJ3/tz1l7rb262m6oqn2AjwLHjJHf4qaQ0B5av26j7k2SJEmSNPNZCJg885KsAK4FdgLOGqf/xcCbkrwB2LWqbgcGgacmeWeSA6pqtCf15yf5IXA5sAfQvXbAl5vP5cDAaBetqqVV1aqqVt/8/onemyRJkiRplrAQMHlub37d3xUI8Oomfhd/+HfeerhzVX0WeBZwO/DNJAdX1Y+BfegUBP4tyVu6L5Dk4XR+6X9yVe0FLOseE7ij+RwC7juJ9yZJkiRJmiV8WJxkVbU+ydHAV5J8BFgD7AtcChw63C/JnwA/raoTkzwM2CvJVcBNVfWZJLcAfz9i+G2B24B1SXYCngGct6m5LlzQT9tF7CRJkiRpTrEQsAVU1eVJVgGHA+8BvpBkMZ1f8Ic9H3hJkjvpvE7wDmA/4N1J7gbuBF41YtyVSS4HrgJ+AXx/i9+MJEmSJGlWcfvAOazValW73Z7uNCRJkiRJk8ztAyVJkiRJEmAhQJIkSZKkOaVnCwFJhpKsSLI6ydeTPLCJ75LkjHHOXZNkh0nM5bwk95pS0cT/u8nzymYdAEmSJEmSelYvLxY4vB0fSU6hsx3fv1fVNXStvj/Zkty3qu7aiFNeVFXtJNsDVyc5uap+N2LMvqoamtxMN9/g2nUMLFk2fkdttjXuziBJkiSpR/TsjIARLgYWACQZSLK6Oe5L8p5m1sCqJK/tOue1SX6YZDDJ7k3//ZNcnOTyJBcl+bMmfmSSryU5Bzg7ybwkn29+5T8TmDeBHLehs7XfUDPmrUn+I8lK4LFJ3pLksibXpUnS9DsvyTuTXJrkx0kOaOJ7NLEVzb3t1sT/sRljdZLXd/2bXJnk40muSPLdJBPJWZIkSZI0x/R8ISBJH/Bk4GujNC8GBoBFVbUXcFpX2w1VtQ/wUeCYJnYVcEBVPRp4C50t+4btAxxaVU+ks23f+qr6c+BYYN8NpHhas1XgfwNv7/rl//7AJVW1d1VdCHyoqvarqj3pFBae2TXGfatqf+D1zfUAXgl8oJkV0QJ+mWRf4GXAXwCPAY5K8uim/27Ah6tqD+AW4HmjJZtkcZJ2kvbQ+nUbuC1JkiRJ0mzUy4WAeUlWANcCOwFnjdLnKcB/Dk/lr6qbutq+3Hwup1MsAOgHvtjMKHgfsEdX/7O6zj8Q+Ewz5ipg1QbyfFFThHgYcEySXZv4EPClrn4HJbkkySBw8Ihrj5brxcCbkrwB2LWqbgeeAJxZVbdV1a3NeQc0/X9WVStGGeceqmppVbWqqtU3v38DtyVJkiRJmo16uRAwvEbArkDorBGwMe5oPof4w1oIbwfObX6V/xtg667+t21GrlTVr4Ef0vm1HuC3w7MDkmwNfITOjIOFwMdHXPteuVbVZ4FnAbcD30xy8Dgp3NF13H3PkiRJkiT9Xs8/LFbV+iRHA19J8pERzWcBr0hyblXdlWT7EbMCRuoH1jbHR26g3/nAC4FzkuwJ7DVenknmA48G3jVK8/BD/w1JtqGz2OF4Ox/8CfDTqjoxycOaHM4HTk5yAp3iyHOAl4yX21gWLuin7SJ2kiRJkjSn9PKMgN+rqsvpTM8/fETTScD/AquaRfleOM5Q7wKOT3I5Gy6CfBTYJsmVwNvoTLUfy2nNKwzLgZOr6l59q+oWOrMAVgPfAS4bJ0+A5wOrm7H3BE6tqh8CJwOXApcAJzX/NpIkSZIkTUiqarpz0DRptVrVbrenOw1JkiRJ0iRLsryqWqO1zYgZAZIkSZIkaXJYCJAkSZIkaQ7p+cUCZ7MkDwVOpbM9YgFLq+oDSbYHTqezBeAa4PlVdXOS3YFPAfsA/1pV72nG2ZrOQoL3o/N/ekZVHTve9QfXrmNgybJJvy/d2xoXZZQkSZLUI5wRML3uAv6pqh4FPAZ4dZJHAUuAs6tqN+Ds5jvATcDRwHtGjHMHcHBV7Q0sAp6e5DFTcQOSJEmSpJnFQsA0qqpfNTsBUFW/Aa4EFgDPBk5pup0CHNL0ub6qLgPuHDFOVdWtzdetmj9XgZQkSZIk3YuFgB6RZAB4NJ1tAXeqql81TdfSeXVgvPP7mq0GrwfOqqpLxui3OEk7SXto/bpJyV2SJEmSNHNYCOgBSbYBvgS8vqr+r7utOvs7jvvrflUNVdUi4CHA/kn2HKPf0qpqVVWrb37/JGQvSZIkSZpJLARMsyRb0SkCnFZVX27C1yXZuWnfmc6v/BNSVbcA5wJPn+xcJUmSJEkzn7sGTKMkAT4BXFlV7+1q+hpwBHBC8/nVccbZEbizqm5JMg94KvDO8a6/cEE/bVezlyRJkqQ5xULA9Ho88BJgsHm/H+BNdAoAX0jycuDnwPMBkjwYaAPbAncneT3wKGBn4JQkfXRmeXyhqr4xpXciSZIkSZoRLARMo6q6EMgYzU8epf+1dNYAGGkVnYUGJUmSJEnaINcIkCRJkiRpDrEQIEmSJEnSHOKrAZMsya1Vtc0kj3kecExVtSdz3MG16xhYsmwyh9QY1rgooyRJkqQe4YwASZIkSZLmEAsBUyDJoiQ/SLIqyZlJtmvi5yV5Z5JLk/w4yQFNfF6Szye5MsmZwLyusQ5PMphkdZJ3dsVvTfLvSVY219ppym9UkiRJktTzLARMjVOBN1TVXsAgcGxX232ran/g9V3xVwHrq+rPm9i+AEl2Ad4JHAwsAvZLckhzzv2BH1TV3sD5wFGjJZJkcZJ2kvbQ+nWTeY+SJEmSpBnAQsAWlqQfeGBVfa8JnQIc2NXly83ncmCgOT4Q+AxAVa2isz0gwH7AeVX166q6Czita6zfAd8YZax7qKqlVdWqqlbf/P7NuDNJkiRJ0kxkIWD63dF8DrF5izfeWVU1SWNJkiRJkmYpHxa3sKpal+TmJAdU1QXAS4DvjXPa+cALgXOS7Ans1cQvBU5MsgNwM3A48MFNzW3hgn7armYvSZIkSXOKhYDJNz/JL7u+vxc4AvhYkvnAT4GXjTPGR4FPJbkSuJLOVH+q6ldJlgDnAgGWVdVXJ/sGJEmSJEmzV/4wm1xzTavVqna7Pd1pSJIkSZImWZLlVdUarc01AiRJkiRJmkMsBMwySfqSXJ7kG+P3liRJkiTNNa4RMPu8js66AtuO13Fw7ToGlizb8hmJNS7KKEmSJKlHOCNgFknyEOCvgZOmOxdJkiRJUm+yEDC7vB/4F+Du6U5EkiRJktSbLATMEkmeCVxfVcvH6bc4STtJe2j9uinKTpIkSZLUKywEzB6PB56VZA3weeDgJJ8Z2amqllZVq6paffP7pzpHSZIkSdI0sxAwS1TVG6vqIVU1ALwAOKeqXjzNaUmSJEmSeoy7BsxhCxf003Y1e0mSJEmaUywEzEJVdR5w3jSnIUmSJEnqQb4aIEmSJEnSHGIhQJIkSZKkOcRCQA9JckiSSrJ78/0+SU5MsjrJYJLLkjy8aVuTZIfm+MFJPp/k6iTLk3wzySOn814kSZIkSb3JNQJ6y+HAhc3nscBhwC7AXlV1d5KHALd1n5AkwJnAKVX1gia2N7AT8OMNXWxw7ToGliyb9JvQva1xUUZJkiRJPcJCQI9Isg3wBOAg4Ot0CgE7A7+qqrsBquqXo5x6EHBnVX1sOFBVK7d8xpIkSZKkmchXA3rHs4FvV9WPgRuT7At8AfibJCuS/EeSR49y3p7A8qlMVJIkSZI0c1kI6B2HA59vjj8PHN7MAPgz4I3A3cDZSZ68ORdJsjhJO0l7aP26zUpYkiRJkjTz+GpAD0iyPXAwsDBJAX1AJfnnqroD+BbwrSTXAYcAZ3edfgVw6ESvVVVLgaUA99t5t5qkW5AkSZIkzRAWAnrDocCnq+oVw4Ek3wMOSPI/VXVNkvsAewGrRpx7DvCOJIubh3yS7AX0V9UFG7rowgX9tF3ETpIkSZLmFF8N6A2H01n5v9uXgFOArydZTacAcBfwoe5OVVXAc4CnNNsHXgEcD1y7xbOWJEmSJM046TxHai5qtVrVbrenOw1JkiRJ0iRLsryqWqO1OSNAkiRJkqQ5xEKAJEmSJElziIWAMSQZSrIiycokP0zyuCQLm9iKJDcl+Vlz/F9JtkpyQpKfNP0vTvKMUcYds1+SW8fI5ZVJXrql71mSJEmSNPu5a8DYbq+qRQBJngYcX1VPBIZjJwPfqKozmu8nADsDe1bVHUl2Ap44yrhvn2C/36uqj03SPd3D4Np1DCxZtiWG1ghr3J1BkiRJUo+wEDAx2wI3j9WYZD5wFPDwqroDoKquA76wsf2S/DvwTOB24NlVdV2S44Bbq+o9Sc4DLgEOAh4IvLyqLkgyAHwauH8z1Guq6qLNu21JkiRJ0mzjqwFjm9dM+78KOInOL/lj+VPgf6vq/8YZc7x+9wd+UFV7A+fTKRqM5r5VtT/weuDYJnY98NSq2gc4DDhxnFwkSZIkSXOQMwLG1v1qwGOBU5PsWVt2v8XfAd9ojpcDTx2j35e7+gw0x1sBH0qyCBgCHjnaiUkWA4sB+rbdcfMzliRJkiTNKM4ImICquhjYARjryfl/gIcl2Xacocbrd2dXoWGIsQs1d4zS5x+A64C9gRbwR6OdWFVLq6pVVa2++f3jpCtJkiRJmm2cETABSXYH+oAbR2uvqvVJPgF8IMkrqup3SXYEnlRVX9zYfpuoH/hlVd2d5Igm3w1auKCftovYSZIkSdKc4oyAsQ2vEbACOB04oqqGNtD/zcCvgR8lWU1niv9oawFMtN/G+ghwRJKVwO7AbZMwpiRJkiRplsmWfeVdvazValW73Z7uNCRJkiRJkyzJ8qpqjdbmjABJkiRJkuYQCwGSJEmSJM0hFgI2QpIHJ/l8kquTLE/yzSRjbdO3S5IzJjDmrZOfqSRJkiRJo3PXgAlKEuBM4JSqekET2xvYCfjxyP5VdQ1w6JQmuZEG165jYMmy6U5jTljj7gySJEmSeoQzAibuIODOqvrYcKCqVgIXJnl3ktVJBpMcBpBkoNkVgCRHJvlykm8n+UmSd3UPnOR9Sa5IcnaznSBJjkpyWZKVSb6UZH4T/9vmWiuTnN/E+pocLkuyKskrpuafRJIkSZI001gImLg9geWjxJ8LLAL2Bp4CvDvJzqP0WwQcBiwEDkvy0CZ+f6BdVXsA3wOObeJfrqr9qmpv4Erg5U38LcDTmvizmtjLgXVVtR+wH3BUkodv+q1KkiRJkmYrCwGb7wnA56pqqKquo/Mwv98o/c6uqnVV9VvgR8CuTfxu4PTm+DPNeAB7JrkgySDwImCPJv594OQkRwF9TewvgZcmWQFcAjwI2G20ZJMsTtJO0h5av24Tb1mSJEmSNFO5RsDEXcHmvfN/R9fxEGP/21fzeTJwSFWtTHIk8CSAqnplkr8A/hpYnmRfIMBrq+o74yVRVUuBpQD323m3Gqe7JEmSJGmWsRAwcecA70iyuHmYJslewC10pvqfAmwPHAj8M7D1BMe9D50Cw+eBFwIXNvEHAL9KshWdGQFrm2s+oqouAS5J8gzgocB3gFclOaeq7mx2MlhbVbdt6MILF/TTdhE7SZIkSZpTLARMUFVVkucA70/yBuC3wBrg9cA2wEo6v+b/S1Vdm2RggkPfBuyf5M3A9XTWEQD4/+hM8/918/mAJv7uJLvRmQVwdnPdVcAA8MNmd4NfA4ds6r1KkiRJkmavVDk7fK5qtVrVbrenOw1JkiRJ0iRLsryqWqO1uVigJEmSJElziIUASZIkSZLmENcI6EFJ1gC/obO7QB/w5qr66rQmJUmSJEmaFSwE9K6DquqGJH8GfBeY9ELA4Np1DCxZNtnDahRr3J1BkiRJUo/w1YBpluTFSS5NsiLJfybpG9FlW+Dmrv5fSbI8yRVJFjexv0vy/q4+RyV539TcgSRJkiRpJrEQMI2S/Dmd7QIfX1WL6LwK8KKm+dwkq4HvAW/uOu3vqmpfoAUcneRBwBeAv0myVdPnZcAnx7jm4iTtJO2h9esm/6YkSZIkST3NVwOm15OBfYHLkgDMA65v2oZfDXgEcHaS86rqVjoP/89p+jwU2K2qfpDkHOCZSa4EtqqqwdEuWFVLgaUA99t5N/eOlCRJkqQ5xkLA9ApwSlW98R7B5Mjh46q6Osl1wKOSzAeeAjy2qtYnOQ/Yuul6EvAm4CrgU1OQuyRJkiRpBrIQML3OBr6a5H1VdX2S7YEHdHdI8sfAw4GfA48Bbm6KALs33wGoqkuSPBTYB9hrIhdfuKCftovYSZIkSdKcYiFgGlXVj5K8GfhukvsAdwKvbprPTTIEbAUsqarrknwbeGUz/f+/gR+MGPILwKKquhlJkiRJkkZhIWCaVdXpwOkjwgNj9L0DeMYGhnsC4G4BkiRJkqQxuWvALJDkgUl+DNxeVWdPdz6SJEmSpN7ljIBZoKpuAR453XlIkiRJknqfMwKmQJKhJCuSrEzywySPa+K7JDmjOV6U5K+6zjkyya+b836U5Kjpyl+SJEmSNHs4I2Bq3F5ViwCSPA04HnhiVV0DHNr0WQS0gG92nXd6Vb2m2TngiiRfq6rrJiupwbXrGFiybLKG0wascXcGSZIkST3CGQFTb1vgZoAkA0lWJ/kj4G3AYc0MgMO6T6iq64GrgV2TfDRJO8kVSd7ajHNwkq8M90/y1CRnTtkdSZIkSZJmDGcETI15SVYAWwM7Awd3N1bV75K8BWhV1Wug82rAcHuSPwH+BPgf4F+r6qYkfcDZSfYCzgU+kmTHqvo18DLgk6MlkmQxsBigb9sdJ/cuJUmSJEk9zxkBU+P2qlpUVbsDTwdOTZIJnHdYU0D4HPCKqroJeH6SHwKXA3sAj6qqAj4NvDjJA4HHAt8abcCqWlpVrapq9c3vn4RbkyRJkiTNJM4ImGJVdXGSHYCJ/Bx/+vAMAYAkDweOAfarqpuTnExnlgHAp4CvA78FvlhVd01u5pIkSZKk2cBCwBRLsjvQB9wIzO9q+g3wgHFO3xa4DViXZCfgGcB5AFV1TZJrgDcDT5lILgsX9NN2ETtJkiRJmlMsBEyN4TUCAAIcUVVDI94OOBdY0vQ7frRBqmplksuBq4BfAN8f0eU0YMequnJSs5ckSZIkzRoWAqZAVfWNEV8D7Nkc3wTsN6LLyaOcc+QGLvUE4OObkqMkSZIkaW6wEDBLJFlO57WBf5ruXCRJkiRJvctCwCxRVftOdw6SJEmSpN5nIaAHJBkAnlRVJzffjwTeDaxtunyoqk5q2o6gsyAgwL9V1SlNfF86rxLMA74JvK7ZVnBMg2vXMbBk2STeicayxkUZJUmSJPWI+0x3AnNdklcB3wLenuS8JA9umk6vqkXN33ARYHvgWOAvgP2BY5Ns1/T/KHAUsFvz9/SpvA9JkiRJ0szgjIBplOQBwFvpPLTvRWcrwNs2cMrTgLOahQVJchbw9CTnAdtW1Q+a+KnAIXQKDJIkSZIk/Z4zAqbX3UAB20NnF4Gq+k3T9rwkq5KckeShTWwBnW0Dh/2yiS1ojkfG7yXJ4iTtJO2h9esm8VYkSZIkSTOBhYBpVFW30ZnOfzydVwPek2Q+8HVgoKr2As4CTpnEay6tqlZVtfrm90/WsJIkSZKkGcJCwDSrqq8Bfwu8C9gR+KequrGq7mi6nAQM7wiwFnho1+kPaWJrm+ORcUmSJEmS7sE1AqZRkm2ABzVffwNcCWyfZOeq+lUTf1YTB/gO8I6uBQL/EnhjVd2U5P+SPAa4BHgp8MHxrr9wQT9tV7OXJEmSpDnFQsD02gr4TzrFgB2A/wVeCByd5FnAXcBNwJEAzQP/24HLmvPfNrxwIPD/+MP2gd/ChQIlSZIkSaPIOFvNawokGQCeVFUnT+V1W61WtdvtqbykJEmSJGkKJFleVa3R2lwjoDfcAqyY7iQkSZIkSbOfrwb0gKqyECBJkiRJmhIWAiZJkiFgEAgwBLymqi5KsgtwYlUdmmQRsEtVfXOSrvk24Pyq+q9NOX9w7ToGliybjFQ0jjUuyihJkiSpR1gImDy3V9UigCRPA44HnlhV1wCHNn0WAS1gUgoBVfWWyRhHkiRJkjR3uEbAlrEtcDN0FgJMsjrJHwFvAw5LsiLJYUmOS3JKkguS/DzJc5O8K8lgkm8n2aoZ4y1JLmvGWZokTfzkJIc2x2uSvDXJD5vzd5+me5ckSZIk9TALAZNnXvOAfxVwEvD27saq+h3wFuD0qlpUVac3TY8ADgaeBXwGOLeqFgK3A8PzyT9UVftV1Z50tgd85hg53FBV+wAfBY4ZrUOSxUnaSdpD69dt8s1KkiRJkmYmCwGT5/bmAX934OnAqcO/3I/jW1V1J531BfqAbzfxQWCgOT4oySVJBukUDfYYY6wvN5/Lu869h6paWlWtqmr1ze+fQHqSJEmSpNnENQK2gKq6OMkOwI4T6H5Hc87dSe6sqmridwP3TbI18BGgVVW/SHIcsPWGxqKzWKH/t5IkSZKke/FhcQto3s/vA24E5nc1/QZ4wEYON/zQf0OSbegsPHjGZicJLFzQT9vV7CVJkiRpTrEQMHnmJVnRHAc4oqqGRrwdcC6wpOl3/EQGrapbknwcWA1cC1w2iTlLkiRJkuaY/GEmuuaaVqtV7XZ7utOQJEmSJE2yJMurqjVam4sFSpIkSZI0h/hqQA9JMkRnt4Bhh1TVmmlKR5IkSZI0C1kI6C23V9WiyRosyX2r6q6x2gfXrmNgybLJupw2YI2LMkqSJEnqEb4a0OOSLErygySrkpyZZLsmfl6SVnO8Q5I1zfGRSb6W5Bzg7OnLXJIkSZLUiywE9JZ5SVY0f2c2sVOBN1TVXnReGzh2AuPsAxxaVU/cUolKkiRJkmYmXw3oLfd4NSBJP/DAqvpeEzoF+OIExjmrqm4arSHJYmAxQN+2O25mupIkSZKkmcYZATPXXfzh/2/rEW23jXVSVS2tqlZVtfrm92+x5CRJkiRJvclCQA+rqnXAzUkOaEIvAYZnB6wB9m2OD53i1CRJkiRJM5SvBvS+I4CPJZkP/BR4WRN/D/CFZqr/Ji39v3BBP21Xs5ckSZKkOSVVNd05aJq0Wq1qt9vTnYYkSZIkaZIlWV5VrdHafDVAkiRJkqQ5xEKAJEmSJElzyJQWApIMJVmRZGWSHyZ5XBPfJckZzfGiJH+1Gdf4ZpIHbkT/45KsbfJakeSETb32BK+3JskO4w5D8ckAACAASURBVPR504jvF23JnCRJkiRJc8eUrhGQ5Naq2qY5fhrwpqp64og+RwKtqnrNFOV0HHBrVb1niq63hs793bCBPr//d9qS7rfzbrXzEe/f0pcRsMZFGSVJkiRNoV5dI2Bb4GaAJANJVif5I+BtwGHNr/OHJdkmyaeSDCZZleR5zTmHN7HVSd45POjwL+7NmFcm+XiSK5J8N8m8iSSWjnc3Yw8mOayJPynJN7r6fagpXAxf963NTIfBJLs38Qc1174iyUlAus7/SpLlTdviJnYCMK+5/9Oa2K0TyOu8JGckuSrJaUl+fx1JkiRJkoZN9faB85KsALYGdgYO7m6sqt8leQtdMwKah/x1VbWw+b5dkl2AdwL70ikmfDfJIVX1lRHX2w04vKqOSvIF4HnAZ0bJ6x+SvLg5fgOwDbAI2BvYAbgsyfkTuL8bqmqfJP8POAb4e+BY4MKqeluSvwZe3tX/76rqpqZAcVmSL1XVkiSvqapFo4z/3A3k9WhgD+Aa4PvA44ELJ5CzJEmSJGkOmeoZAbdX1aKq2h14OnDqBH65fgrw4eEvVXUzsB9wXlX9uqruAk4DDhzl3J9V1YrmeDkwMMY13tfktaiqvgM8AfhcVQ1V1XXA95prjufLo1zrQJriQ1Uto5kF0Tg6yUrgB8BD6RQuNmRDeV1aVb+sqruBFWPda5LFSdpJ2kPr103gliRJkiRJs8m0vRpQVRfT+VV7xy14mTu6jofY/BkQd3HPf7Otx7jeuNdK8iQ6RY7HVtXewOWjjLcxJnSvVbW0qlpV1eqb378Zl5MkSZIkzURT/WrA7zXv0PcBNwLzu5p+Azyg6/tZwKuB1zfnbQdcCpzYrL5/M3A48MFJTO8C4BVJTgG2p/Or/j8DWwGPSnI/YB7wZMaffn8+8ELg35I8A9iuifcDN1fV+ubf4jFd59yZZKuqunOCee2+KTe5cEE/bRexkyRJkqQ5ZapnBAwvgrcCOB04oqqGRvQ5l87D9opmMbx/A7ZrFshbCRxUVb8CljR9VwLLq+qrk5jnmcCqZuxzgH+pqmur6hfAF4DVzeflExjrrcCBSa6g847//zbxbwP3TXIlcAKd1wOGLQVWDS8WOF5em3B/kiRJkqQ5akq3D1RvabVa1W63pzsNSZIkSdIk69XtAyVJkiRJ0hSzECBJkiRJ0hxiIWAjJKkk/9H1/Zgkx03S2FsnuSrJwq7YPyf5zwmef1ySYyYjF0mSJEnS7DVtuwbMUHcAz01yfFXdMJkDV9Vvk7we+EiSA4FdgFcCo77T0S3JJv0/Dq5dx8CSZZtyqjbSGndnkCRJktQjnBGwce6is6L/P4xsSLJjki8luaz5e3wTH0zywHTcmOSlTfzUJE/tHqOqvg38Cngp8D7gOKA/yTlJViU5O8nDmvNPTvKxJJcA7xqRy1FJvpVk3qT/C0iSJEmSZjQLARvvw8CLkvSPiH8AeF9V7Qc8DzipiX8feDywB/BT4IAm/ljgolHGfz3w78COVfVp4IPAKVW1F3AacGJX34cAj6uqfxwOJHkN8EzgkKq6fZPvUpIkSZI0K/lqwEaqqv9LcipwNND9oP0U4FFJhr9vm2Qb4ALgQODnwEeBxUkWADdX1W2jjH9NknOAbzShxwLPbY4/zT1//f9iVQ11fX8p8As6RYA7R8s/yWJgMUDftjtO7KYlSZIkSbOGMwI2zfuBlwP374rdB3hMVS1q/hZU1a3A+XRmARwAnAf8GjiUToFgLHc3f+MZWUgYBAbozBQYVVUtrapWVbX65o+c1CBJkiRJmu2cEbAJquqmJF+gUwz4ZBP+LvBa4N0ASRZV1Yqq+kWSHYA/qqqfJrkQOAZ4zQQvdxHwAjqzAV7EhgsIl9OZdfC1JE+rqms2NPDCBf20XcROkiRJkuYUZwRsuv8Aduj6fjTQahb1+xGdFf+HXQL8uDm+AFgAXDjB67wWeFmSVcBLgNdtqHNVDRcaljUFCEmSJEmSfi9VNd05aJq0Wq1qt9vTnYYkSZIkaZIlWV5Vo25H74wASZIkSZLmEAsBkiRJkiTNIRYCekySW7uO/yrJj5PsmuS4JOuT/PEYfR+c5PNJrk6yPMk3kzxyqvOXJEmSJPU2dw3oUUmeDJwIPK2qfp4E4Abgn4A3jOgb4EzglKp6QRPbG9iJPyxSeC+Da9cxsGTZlrkB3cMad2eQJEmS1COcEdCDkhwIfBx4ZlVd3dX0SeCwJNuPOOUg4M6q+thwoKpWVtWGthqUJEmSJM1BFgJ6z/2ArwCHVNVVI9pupVMMGLmF4J7A8okMnmRxknaS9tD6dZudrCRJkiRpZrEQ0HvuBC4CXj5G+4nAEUkesCmDV9XSqmpVVatvfv+m5ihJkiRJmqEsBPSeu4HnA/snedPIxqq6Bfgs8Oqu8BXAvlOTniRJkiRpJnOxwB5UVeuT/DVwQZLrquoTI7q8F7iMP/z/nQO8I8niqloKkGQvoH9D6wQsXNBP20XsJEmSJGlOcUZAj6qqm4CnA29O8qwRbTfQ2SXgfs33Ap4DPKXZPvAK4Hjg2qnNWpIkSZLU69J5htRc1Gq1qt1uT3cakiRJkqRJlmR5VbVGa3NGgCRJkiRJc4iFAEmSJEmS5hALAVMoyYOSrGj+rk2ytuv7H23gvCcl+cZU5ipJkiRJmp3cNWAKVdWNwCKAJMcBt1bVe6Yrn8G16xhYsmy6Lj+nrHF3BkmSJEk9whkB0yzJk5NcnmQwySeT3K+JPz3JVUl+CDy3q//+SS5uzrkoyZ818fOTLOrqd2GSvaf8hiRJkiRJPc1CwPTaGjgZOKyqFtKZofGqJFsDHwf+BtgXeHDXOVcBB1TVo4G3AO9o4p8AjgRI8khg66paOfKCSRYnaSdpD61ft0VuSpIkSZLUuywETK8+4GdV9ePm+ynAgcDuTfwn1dnf8TNd5/QDX0yyGngfsEcT/yLwzCRbAX9Hp8BwL1W1tKpaVdXqm98/6TckSZIkSeptFgJmnrcD51bVnnRmDGwNUFXrgbOAZwPPB06btgwlSZIkST3LxQKn1xAwkORPq+p/gJcA36Mz/X8gySOq6mrg8K5z+oG1zfGRI8Y7Cfg6cEFV3TzexRcu6KftInaSJEmSNKc4I2B6/RZ4GZ2p/oPA3cDHquq3wGJgWbNY4PVd57wLOD7J5Ywo5FTVcuD/gE9NRfKSJEmSpJknnVfQNRsk2QU4D9i9qu4er3+r1ap2u73F85IkSZIkTa0ky6uqNVqbMwJmiSQvBS4B/nUiRQBJkiRJ0tzkGgGzRFWdCpw63XlIkiRJknqbMwJ6TJKBZmvA7thxSY4ZEdspyTeSrEzyoyTfnNpMJUmSJEkzkTMCZq63AWdV1QcAkuy1sQMMrl3HwJJlk56Y7m2NuzNIkiRJ6hHOCJi5dgZ+OfylqlZNYy6SJEmSpBnCQsDM9WHgE0nOTfKvzY4B40qyOEk7SXto/botnKIkSZIkqddYCOg9Y+3neI94VX0H+BPg48DuwOVJdhx38KqlVdWqqlbf/P7NTlaSJEmSNLNYCOg9NwLbjYhtD9wwsmNV3VRVn62qlwCXAQdOQX6SJEmSpBnMxQJ7TFXdmuRXSQ6uqnOSbA88HfhAd78kBwM/qKr1SR4APAL434251sIF/bRdxE6SJEmS5hQLAb3ppcCHk7y3+f7Wqro6ySsBqupjwL7Ah5LcRWdmx0lVdRlAkhVVtWg6EpckSZIk9bZUjfVKuma7VqtV7XZ7utOQJEmSJE2yJMurqjVam2sESJIkSZI0h1gIkCRJkiRpDnGNgC0syRAwCAQYAl5TVReN0feiqnpckgHgcVX12SbeAl5aVUePcd6TgGOq6pkbk9vg2nUMLFm2MaeoB61xwUdJkiRJG8EZAVve7VW1qKr2Bt4IHD+yQ5L7AlTV45rQAPDC4faqao9VBJAkSZIkaWNYCJha2wI3Q+dX/CQXJPka8KMmdmvT7wTggCQrkvxD0/cbTZ8nNvEVSS5vtg4E2CbJGUmuSnJakkzxvUmSJEmSZgBfDdjy5iVZAWwN7Awc3NW2D7BnVf1sxDlL6Jrq30z9H3YM8Oqq+n6SbYDfNvFHA3sA1wDfBx4PXDgymSSLgcUAfdvuuHl3JkmSJEmacZwRsOUNvxqwO/B04NSuX+svHaUIMJ7vA+9NcjTwwKq6q2usX1bV3cAKOq8X3EtVLa2qVlW1+ub3b/zdSJIkSZJmNAsBU6iqLgZ2AIZ/ir9tE8Y4Afh7YB7w/SS7N013dHUbwtkekiRJkqRR+LA4hZqH9j7gxnG6/gZ4wGgNSR5RVYPAYJL9gN2BWzYln4UL+mm74rwkSZIkzSkWAra84TUCoLOF4BFVNTTOWn6rgKEkK4GTgcu72l6f5CDgbuAK4FvAYyc9a0mSJEnSrJSqmu4cNE1arVa12+3pTkOSJEmSNMmSLK+q1mhtrhEgSZIkSdIcYiFAkiRJkqQ5xDUCelCSBwFnN18fTGcXgF833/evqt9NxnUG165jYMmyyRhK02iNCz5KkiRJ2ggWAnpQVd0ILAJIchxwa1W9Z1qTkiRJkiTNCr4aMDPcJ8lygCR7J6kkD2u+X51kfpKBJOckWZXk7OF2SZIkSZK6WQiYGe4Gtk6yLXAA0AYOSLIrcH1VrQc+CJxSVXsBpwEnjjZQksVJ2knaQ+vXTVH6kiRJkqReYSFg5rgIeDxwIPCO5vMA4IKm/bHAZ5vjTwNPGG2QqlpaVa2qavXN79+yGUuSJEmSeo6FgJnjfDoP/rsCXwX2pvOwf8GGTpIkSZIkqZuLBc4cFwD/DpxfVXcnuQn4K+CNTftFwAvozAZ4ERMoECxc0E/bFeclSZIkaU5xRsAMUVVrgNCZGQBwIXBLVd3cfH8t8LIkq4CXAK+b8iQlSZIkST0vVTXdOWiatFqtarfb052GJEmSJGmSJVleVa3R2pwRIEmSJEnSHDKrCwFJhpKsSLI6ydeTPHALXGMgyeoRseOSHNMcn5zk0OZ4TZIdJjDms5Is2ZhrdrUdmWSXjbsLSZIkSdJcMdsXC7y9qhYBJDkFeDWdBfd6VpL7VtXXgK9t4hBHAquBa8brOLh2HQNLlm3iZdQr1rjgoyRJkqSNMKtnBIxwMbAAIMmiJD9IsirJmUm2a+JHJ/lRE/98E3tiM6tgRZLLkzxgM/P4lySDSS5N8qfNNU5O8rEklwDvan7V/1DTtlOT48rm73HNOH1JPp7kiiTfTTKvmXnQAk5r8p23mblKkiRJkmaZOVEISNIHPJk//Mp+KvCGqtoLGASObeJLgEc38Vc2sWOAVzczCw4Abt/MdNZV1ULgQ8D7u+IPAR5XVf84ov+JwPeqam9gH+CKJr4b8OGq2gO4BXheVZ0BtIEXVdWiqtrcXCVJkiRJs8xsLwTMS7ICuBbYCTgrST/wwKr6XtPnFODA5ngVnV/TXwzc1cS+D7w3ydHNeXdxT2NtuzBW/HNdn4/tin+xqoZG6X8w8FGAqhqqqnVN/GdVtaI5Xg4MjHG9e0iyOEk7SXto/brxT5AkSZIkzSqzvRAwvEbArkDorBGwIX8NfJjOL++XNe/rnwD8PTAP+H6S3UeccyOw3YjY9sANY1yjxji+bZzcRrqj63iICa73UFVLq6pVVa2++f0beUlJkiRJ0kw32wsBAFTVeuBo4J/oPHDfnOSApvklwPeS3Ad4aFWdC7wB6Ae2SfKIqhqsqncClwG7jxj7VuBXSQ4GSLI98HTgwjHSOazr8+IJpH828Kpm7L5mRsOG/AbY3HUMJEmSJEmz1GzfNeD3quryJKuAw4EjgI8lmQ/8FHgZ0Ad8pnnQDnBiVd2S5O1JDgLupvN+/rdGGf6lwIeTvLf5/taqunqMVLZr8rijyWU8rwOWJnk5nV/+XwX8agP9T27u7XbgsRtaJ2Dhgn7arjgvSZIkSXNKqsZ6lV2zXavVqna7Pd1pSJIkSZImWZLlVdUarW1OvBogSZIkSZI6LARIkiRJkjSHWAiYJZJ8Msn1SVZPdy6SJEmSpN41ZxYLnANOBj4EnDrREwbXrmNgybItlpCmxhoXfJQkSZK0EZwRMEtU1fnATdOdhyRJkiSpt1kIkCRJkiRpDrEQMMckWZyknaQ9tH7ddKcjSZIkSZpiFgLmmKpaWlWtqmr1ze+f7nQkSZIkSVPMxQLnsIUL+mm70JwkSZIkzSnOCJglknwOuBj4syS/TPLy6c5JkiRJktR7nBEwS1TV4dOdgyRJkiSp9zkjQJIkSZKkOcRCgCRJkiRJc4iFgC0kyYOTfD7J1UmWJ/lmkkdOd16SJEmSpLnNNQK2gCQBzgROqaoXNLG9gZ2AH2/GmKmquycrz8G16xhYsmyyhtM0WePOD5IkSZI2gjMCtoyDgDur6mPDgapaCRyV5JDhWJLTkjw7yZFJvprkvCQ/SXJs0z6Q5L+TnAqsBh6a5Nau8w9NcnJz/LdJVidZmeT8KbpPSZIkSdIMYyFgy9gTWD5K/BPAkQBJ+oHHAcM/ye8PPA/YC/jbJK0mvhvwkarao6p+voFrvgV4WlXtDTxrs+9AkiRJkjQrWQiYQlX1PWC3JDsChwNfqqq7muazqurGqrod+DLwhCb+86r6wQSG/z5wcpKjgL6xOiVZnKSdpD20ft2m34wkSZIkaUayELBlXAHsO0bbqcCLgZcBn+yK14h+w99vGyMOsPXvg1WvBN4MPBRYnuRBo128qpZWVauqWn3z+zd4E5IkSZKk2cfFAreMc4B3JFlcVUsBkuwF9AMnA5cC11bVj7rOeWqS7YHbgUOAvxtj7OuS/Dnw38BzgN804z+iqi4BLknyDDoFgRs3lOTCBf20XWhOkiRJkuYUZwRsAVVVdB7Sn9JsH3gFcDydh//rgCuBT4047VLgS8AqOq8MtMcYfgnwDeAi4Fdd8XcnGUyyumlbOWk3JEmSJEmaNZwRsIVU1TXA80fGk8ynswDg50Y0/bKqDukOVNUaOgsPdsfOAM4Y5XrP3cyUJUmSJElzgDMCplCSp9CZDfDBqnKlPkmSJEnSlHNGwBSqqv8Cdh0lfjKdtQMkSZIkSdqinBHQQ5Ksad7zX5Gk3cS2T3JWkp80n9s18ecluSLJBcM7BCR5RJLTp/MeJEmSJEm9LZ117dQLkqwBWlV1Q1fsXcBNVXVCkiXAdlX1hiTnAX8FPLeJfTDJ54C3VNVPJnK9++28W+18xPsn/T40tda484MkSZKkEZIsr6rWaG3OCOh9zwZOaY5PobO1IMDdwP2A+cCdSQ6gsyvBhIoAkiRJkqS5yTUCeksB301SwH9W1VJgp6oa3ibwWmCn5vh44L+Aa4AXA18EXjDeBZIsBhYD9G274+RmL0mSJEnqeRYCessTqmptkj8GzkpyVXdjVVVTJKCqzgLOAkjyUuCbwCOTHAPcDLyuqtaPvEBTXFgKnVcDtujdSJIkSZJ6jq8G9JCqWtt8Xg+cCewPXJdkZ4Dm8/ruc5LMB44EPgy8FTgCuBB40ZQlLkmSJEmaMZwR0COS3B+4T1X9pjn+S+BtwNfoPNyf0Hx+dcSp/wycWFV3JplH5/WCu+msHbBBCxf003ahOUmSJEmaUywE9I6dgDOTQOf/5bNV9e0klwFfSPJy4OfA84dPSLILsH9VvbUJffD/Z+/e4+wq63uPf74GBCIwoFJMo+1YikUggLKhBdGCt1OtF7BUQVrBKtEeteoRbdrqKVateKRegCqNt3hBUUEqTaiKlwgiIDuQZLjVa1qZooiWEYgghN/5Y6/RMc5MJmFm9p7Zn/frtV97ree2fivzT9ZvP+t5gKuA2/jlooKSJEmSJP2C2wf2sVarVe12u9thSJIkSZKmmdsHSpIkSZIkwESAJEmSJEl9xUTAFiR5SJK1zecHSYbHnD9ws7anNtv3kWRFkmO34jqDSa6d7vglSZIkSRrLxQK3oKp+DBwEnQd94I6qOr2rQU2ToeERBpet6nYYup82uPODJEmSpK3gjIBtkOTkJFclWZfk/CSTbtWX5P827a9NsjzN1gBJDm7GWAe8bEz7BUne3vRZn+QlTfkDkrwnyY1JLk5y0eisg4muIUmSJEnSWCYCts1nquqQqjoQuAF40Rban9W03x/YCXhGU/4h4BXNOGO9CBipqkOAQ4CTkzwSeA4wCOwL/Dlw2BSu8SuSLE3STtLetHFkqvcrSZIkSZonTARsm/2TXJpkCDgB2G8L7Y9KcmXT/onAfkl2A3arqkuaNh8d0/6pwAuSrAWuBB4C7A0cAXy6qu6rqh8AX5nsGuMFUlXLq6pVVa0FCwe27q4lSZIkSXOeawRsmxXA0VW1LslJwJETNUyyI/AeoFVV32/WGdhxC+OHzkyBz2821tOn8RqSJEmSpD5kImDb7ALcnGR7OjMChidpO/pAfmuSnYFjgfOq6rYktyU5oqq+1owz6vPAXyb5clXdk+RRzTUuA05M8mFgDzoJiI9PdI0t3cSSxQO0XWhOkiRJkvqKiYBt8wY6U/Z/1HzvMlHD5oH/fcC1wA+Aq8ZUvxD4YJICvjCm/P101gK4uln070fA0cD5wJOA64HvA1fTWUtgsmtIkiRJkvQLqapux6CtkGTnqrojyUOAbwCPa9YL2GqtVqva7fb0BihJkiRJ6roka6qqNV6dMwLmnpXNQoMPBN60rUkASZIkSVJ/MhEwx1TVkd2OQZIkSZI0d5kImGXNlP4vNacPAzbRWQMA4NCq+vlsxTI0PMLgslWzdTnNkA0u+ChJkiRpK5gImGVV9WPgIIBmm787qur0rgYlSZIkSeobD+h2AIIkByf5apI1ST6fZFGSgST/keT3mjafSHJyc/zeJO0k1yV545hxTktyfZL1SUwuSJIkSZJ+jTMCui/AmcCzq+pHSZ4HvKWq/iLJy4EVSd4N7F5V72v6/F1V/STJAuBLSQ4AhoFjgH2qqpoFBX/9YslSYCnAgl33mOFbkyRJkiT1GhMB3bcDsD9wcRKABcDNAFV1cZI/Bf4ZOHBMn+c2D/TbAYuAfYHrgbuADyRZCawc72JVtRxYDrDDor3dO1KSJEmS+oyJgO4LcF1VHfZrFckDgEcDG4HdgZuSPBI4BTikqv4nyQpgx6q6N8mhwJOAY4GXA0+cpXuQJEmSJM0RJgK6725gjySHVdXlSbYHHlVV1wGvBm4A/hb4UJLDgF2BO4GRJHsCTwNWJ9kZWFhVFyW5DPjuli68ZPEAbVeclyRJkqS+YiKg++6j8wv+GUkG6PxN3pXkXuDFdLYUvD3JJcDrq+rvk1wD3Ah8H7isGWcX4LNJdqQzy+D/zPaNSJIkSZJ6X6p8TbxftVqtarfb3Q5DkiRJkjTNkqypqtZ4dW4fKEmSJElSHzERIEmSJElSH3GNgB6U5OF0tgzcl06yZiXw2qr6+XReZ2h4hMFlq6ZzSHXBBhd8lCRJkrQVnBHQY5IE+Azwr1W1N/AoYGfgLV0NTJIkSZI0LzgjoPc8Ebirqj4EUFWbkrwa+F6S7wFPBhYCewEXVNXrAJI8FXgjsAPwHeCFVXVHN25AkiRJktS7nBHQe/YD1owtqKqfAv9FJ3FzEPA8YAnwvCSPSPJQ4PXAk6vqsUCbCbYPTLI0STtJe9PGkRm8DUmSJElSL3JGwNzzpaoaAUhyPfDbwG501hO4rPNmAQ8ELh+vc1UtB5YD7LBob/eOlCRJkqQ+YyKg91wPHDu2IMmuwG8B9wJ3j6naROdvGODiqjp+toKUJEmSJM1NJgJ6z5eA05K8oKo+kmQB8E/ACmDjBH2uAP45ye9W1beTPAhYXFXfnOxCSxYP0HbFeUmSJEnqK64R0GOqqoBjgD9N8i3gm8BdwN9O0udHwEnAJ5Ksp/NawD4zH60kSZIkaa5J57lT/ajValW73e52GJIkSZKkaZZkTVW1xqtzRoAkSZIkSX3ERIAkSZIkSX3ExQJ7VJJNwNCYoqOBQeCzwPfGlJ9SVV9M8jDgXcAhwG3AD4FXTbZg4NDwCIPLVk136JplG1zwUZIkSdJWMBHQu35WVQeNLUgyCFxaVc/YrDzABcCHq+q4puxAYE86iw1KkiRJkgSYCJgvjgLuqaqzRwuqal0X45EkSZIk9SgTAb1rpyRrm+PvVdUxzfHjx5QD/AmwP7BmKoMmWQosBViw6x7TFaskSZIkaY4wEdC7fu3VgMZ4rwZMedCqWg4sB9hh0d7uHSlJkiRJfcZdA+aH64CDux2EJEmSJKn3OSNgfvgy8I9Jlja/+JPkAGCgqi6dqNOSxQO0XXFekiRJkvqKMwLmnscnWTvmc2xVFXAM8OQk30lyHfBW4AfdDVWSJEmS1GucEdCjqmrnccpWAwMTtP9v4LkzHJYkSZIkaY5zRoAkSZIkSX3ERMAUJbljuvuk49Ykuzfni5JUkiPGtPlRkockeX+Sfbc+ckmSJEmSfslXA7qoqirJFcBhwEXA4cA1zffXkvwe8OOq+jHw4um+/tDwCIPLVk33sJplG1zwUZIkSdJWcEbAVmh+wX97kmuTDCV5XlO+KMklzeJ91yZ5/Jg+b0myLskVSfYcZ9iv03nwp/l+J53EwOj5Zc04q5O0kixIsmJMDK9u6vdK8rkka5JcmmSfGfpnkCRJkiTNYSYCts5zgIOAA4EnA29Psgh4PvD5qhqtW9u0fxBwRVUdCFwCnDzOmJfxy0TAocAFwCOa88PpJArGOghYXFX7V9US4ENN+XLgFVV1MHAK8J77c6OSJEmSpPnJVwO2zhHAJ6pqE/DDJF8FDgGuAj6YZHvgX6tqNBHwc2Blc7wGeMo4Y14FPCbJg4Dtq+qOJN9N8rt0EgH/tFn77wK/k+RMYBXwhSQ7N20/nWS03Q7j3UCSpcBSgAW77rF1dy9JkiRJmvOcETANquoS4AnAMLAiyQuaqnuqqprjTYyTeKmqjcC3gL8Arm6KrwCeDvwG8B+btf8fOrMOVgMvBd5P5+94W1UdNObzTyMmSQAAIABJREFU6AliXV5VrapqLVg47k6EkiRJkqR5zETA1rkUeF7znv4edB7+v5Hkt4EfVtX76DyYP3Yrx/068Crg8ub8cuCVdF4rqLENkzwUeEBVnQ+8HnhsVf0U+F6SP23aJMmB23aLkiRJkqT5zFcDpiDJdsDddN7fPwxYBxTwuqr6QZITgdcmuQe4A3jBhION7zI6D/6jiYCrgYfTSSpsbjHwoSSjSZy/ab5PAN6b5PXA9sC5TZwTWrJ4gLYrzkuSJElSX8lmPzhrHM2v6++rqkO7Hct0arVa1W63ux2GJEmSJGmaJVlTVa3x6nw1YAuSvBT4BJ1p+JIkSZIkzWm+GrAFVXU2cHa345AkSZIkaTo4I6CHJdmQZCjJ2iTtpuzBSS5O8q3me/emPEnOSPLtJOuTbO2ChZIkSZKkPuCMgN53VFXdOuZ8GfClqjotybLm/K+BpwF7N5/fB97bfE9oaHiEwWWrZiZqzZoNLvgoSZIkaSs4I2DueTbw4eb4w8DRY8o/Uh1XALslWdSNACVJkiRJvctEQG8r4AtJ1iRZ2pTtWVU3N8c/APZsjhcD3x/T96amTJIkSZKkX/DVgN52RFUNJ/kN4OIkN46trKpKslX7PzYJhaUAC3bdY/oilSRJkiTNCc4I6GFVNdx83wJcABwK/HB0yn/zfUvTfBh4xJjuD2/KNh9zeVW1qqq1YOHATIYvSZIkSepBzgjoUUkeBDygqm5vjp8K/ANwIXAicFrz/dmmy4XAy5OcS2eRwJExrxCMa8niAdouNCdJkiRJfcVEQO/aE7ggCXT+Th+vqs8luQr4VJIXAf8JPLdpfxHwdODbwEbghbMfsiRJkiSp15kI6FFV9V3gwHHKfww8aZzyAl42C6FJkiRJkuYw1wiQJEmSJKmPmAiQJEmSJKmPmAjosiR3bGX7I5OsbI6flWTZzEQmSZIkSZqPXCNgDquqC+nsFrBNhoZHGFy2ahoj0nyzwV0lJEmSpHnHGQE9ovmlf3WS85LcmOScNFsGJPmjpuxq4Dlj+pyU5Kzm+JlJrkxyTZIvJtmzS7ciSZIkSephJgJ6y2OAVwH7Ar8DPC7JjsD7gGcCBwMPm6Dv14A/qKrHAOcCr5v5cCVJkiRJc42vBvSWb1TVTQBJ1gKDwB3A96rqW035x4Cl4/R9OPDJJIuABwLfG+8CSZaO9l+w6x7THb8kSZIkqcc5I6C33D3meBNbl6g5EzirqpYALwF2HK9RVS2vqlZVtRYsHNj2SCVJkiRJc5IzAnrfjcBgkr2q6jvA8RO0GwCGm+MTpzLwksUDtF0MTpIkSZL6ijMCelxV3UVnKv+qZrHAWyZoeirw6SRrgFtnKTxJkiRJ0hyTqup2DOqSVqtV7Xa722FIkiRJkqZZkjVV1RqvzhkBkiRJkiT1ERMBkiRJkiT1ERMBc0ySDya5Jcm1Y8oenOTiJN9qvnfvZoySJEmSpN7lGgFzTJInAHcAH6mq/Zuy/wf8pKpOS7IM2L2q/npLY+2waO9adOK7ZjZgzWkb3FVCkiRJmpNcI2AeqapLgJ9sVvxs4MPN8YeBo2c1KEmSJEnSnGEiYH7Ys6pubo5/AOw5UcMkS5O0k7Q3bRyZnegkSZIkST3DRMA8U513PSZ836OqlldVq6paCxYOzGJkkiRJkqReYCJgfvhhkkUAzfctXY5HkiRJktSjtut2AJoWFwInAqc135+dSqcliwdouxicJEmSJPUVZwTMMUk+AVwO/F6Sm5K8iE4C4ClJvgU8uTmXJEmSJOnXOCNgjqmq4yeoetKsBiJJkiRJmpOcESBJkiRJUh8xESBJkiRJUh8xETCNkjwsyblJvpNkTZKLkjxqBq93x0yNLUmSJEman1wjYJokCXAB8OGqOq4pOxDYE/hmN2ObyNDwCIPLVnU7DPWwDe4qIUmSJM07zgiYPkcB91TV2aMFVbUOuCbJl5JcnWQoybMBkgwmuSHJ+5Jcl+QLSXZq6k5OclWSdUnOT7KwKX9kksubcd48ep0kO493DUmSJEmSNmciYPrsD6wZp/wu4JiqeiydZME/NbMHAPYG/rmq9gNuA/6kKf9MVR1SVQcCNwAvasrfDby3qpYAN0/xGr8iydIk7STtTRtHtvlmJUmSJElzk4mAmRfgH5OsB74ILKbzugDA96pqbXO8BhhsjvdPcmmSIeAEYL+m/HHAJ5rjj07xGr+iqpZXVauqWgsWDtzvm5MkSZIkzS2uETB9rgOOHaf8BGAP4OCquifJBmDHpu7uMe02ATs1xyuAo6tqXZKTgCPHtKutvIYkSZIkSb9gImD6fJnOr/JLq2o5QJIDgN8Gbmke0I9qzrdkF+DmJNvTecgfbsovA44DPtaUjxrYhmuwZPEAbReDkyRJkqS+4qsB06SqCjgGeHKzfeB1wFuBi4BWM83/BcCNUxjuDcCVdB78x7Z/JfCyZqzFY8rP2YZrSJIkSZL6UDrPr+pHrVar2u12t8OQJEmSJE2zJGuqqjVenTMCJEmSJEnqIyYCJEmSJEnqIy4WOMOSFHBOVf1Zc74dcDNwZVU9I8mzgH2r6rQkpwJ3VNXpSVYAK6vqvCTvB95RVddPZ2xDwyMMLls1nUNqntngYpKSJEnSvGMiYObdCeyfZKeq+hnwFH65CwBVdSFw4WQDVNWLZzZESZIkSVK/8NWA2XERMPrT6vHAJ0YrkpyU5KzJOidZnaTVHB+fZCjJtUneNqbNHUnekmRdkiuS7DkD9yFJkiRJmuNMBMyOc4HjkuwIHEBna8CtluQ3gbcBTwQOAg5JcnRT/SDgiqo6ELgEOHmCMZYmaSdpb9o4si1hSJIkSZLmMBMBs6Cq1gODdGYDXHQ/hjoEWF1VP6qqe4FzgCc0dT8HVjbHa5rrjRfL8qpqVVVrwcKB+xGKJEmSJGkuco2A2XMhcDpwJPCQGRj/nqqq5ngT/m0lSZIkSePwYXH2fBC4raqGkhy5jWN8AzgjyUOB/6Ezw+DMbQ1oyeIB2q4KL0mSJEl9xUTALKmqm4Az7ucYNydZBnwFCLCqqj47HfFJkiRJkvpDfjmbXP2m1WpVu93udhiSJEmSpGmWZE1Vtcarc7FASZIkSZL6iIkASZIkSZL6iGsETEGSQeDIqloxpuwJwLuAA4Djquq8CfoWcE5V/Vlzvh1wM3BlVT0jybOAfavqtCSnAndU1elJVgArq+q8JO8H3lFV10/nfQ0NjzC4bNV0DilN2QYXqpQkSZK6wkTAFiT5S+CvgJ2TnETnof8HwH8BJwGnbGGIO4H9k+xUVT8DngIMj1ZW1YV0thacUFW9eJtvQJIkSZKkMXw1YBJJdgHeCJwAvIHOg/+dAFW1oarWA/dNYaiLgNGfP48HPjHmGiclOWsLcaxO0mqOj08ylOTaJG8b0+aOJG9Jsi7JFUn2nPKNSpIkSZL6homAyd0HFPBg+MXD/+3bMM65wHFJdqTzKsGV2xJMkt8E3gY8ETgIOCTJ0U31g4ArqupA4BLg5AnGWJqknaS9aePItoQhSZIkSZrDTARMoqrupPNA/VbgTUlOT7JwG8ZZDwzSmQ1w0f0I6RBgdVX9qKruBc4BntDU/RxY2Ryvaa43XizLq6pVVa0FCwfuRyiSJEmSpLnIRMAWNO/w/ynw/4A9gNds41AXAqcz5rWAaXZPVVVzvAnXf5AkSZIkjcOHxUkk2Rl4SHN6O3ADzWsC2+CDwG1VNZTkyG0c4xvAGUkeCvwPnRkGZ27jWCxZPEDbldslSZIkqa+YCJjc9sC/0EkGPJTOTgHPB0hyCHABsDvwzCRvrKr9Jhqoqm4Czrg/wVTVzUmWAV8BAqyqqs/enzElSZIkSf0lv5xNrokkGQSOrKoV3Y1kerVarWq3290OQ5IkSZI0zZKsqarWeHWuETA1twFrux2EJEmSJEn3l68GTEFVmQiQJEmSJM0LJgJ6SJINdBYl3ATcW1WtJH8KnAo8Gji0qtpN28cB76WzbeDxVfWtJLsBnwL+qKru29L1hoZHGFy2akbuRdqSDS5UKUmSJHWFiYDec1RV3Trm/FrgOXQWLRzrNcDTgUHgpc3564F/nEoSQJIkSZLUn0wE9LiqugEgyeZV9wALm889SfYCHlFVq2c1QEmSJEnSnGIioLcU8IUkBfxLVS2fpO1bgY8APwP+HDidzoyASSVZCiwFWLDrHvc7YEmSJEnS3GIioLccUVXDSX4DuDjJjVV1yXgNq2ot8AcASZ4A3Nw5zCfpzBZ4TVX9cJx+y4HlADss2tu9IyVJkiSpz7h9YA+pquHm+xbgAuDQLfVJ552B1wNvAv4eeB3wPuCvZi5SSZIkSdJc5YyAHpHkQcADqur25vipwD9MoesLgIuq6idJFgL3NZ+FW+q4ZPEAbVdulyRJkqS+YiKgd+wJXNAsCrgd8PGq+lySY4AzgT2AVUnWVtX/Amge/E+ikzQAeAdwEZ0tBZ8/u+FLkiRJkuaCVPmaeL9qtVrVbre7HYYkSZIkaZolWVNVrfHqXCNAkiRJkqQ+YiJgliXZlGRtkmuTfLqZ3j9R25OSnNUcvzTJCyZpe2qSU2YiZkmSJEnS/OEaAbPvZ1V1EECSc4CX0nm3f1JVdfZ0BzI0PMLgslXTPaw0JRtcqFKSJEnqCmcEdNelwO8meXCSf02yPskVSQ7YvOHYX/yT/FWS65v2545ptm+S1Um+m8TtAyVJkiRJv8YZAV2SZDvgacDngDcC11TV0UmeCHwEOGiS7suAR1bV3Ul2G1O+D3AUsAvwH0neW1X3zMwdSJIkSZLmImcEzL6dkqwF2sB/AR8AjgA+ClBVXwYekmTXScZYD5yT5M+Ae8eUr6qqu6vqVuAWOlsS/ookS5O0k7Q3bRyZnjuSJEmSJM0ZzgiYfb9YI2BUkq0d44+BJwDPBP4uyZKm/O4xbTYxzt+3qpYDywF2WLS3e0dKkiRJUp9xRkBvuBQ4ASDJkcCtVfXT8RomeQDwiKr6CvDXwACw8yzFKUmSJEma45wR0BtOBT6YZD2wEThxkrYLgI8lGQACnFFVt23DrAKWLB6g7crtkiRJktRXUuXs8H7VarWq3W53OwxJkiRJ0jRLsqaqWuPV+WqAJEmSJEl9xESAJEmSJEl9xETADEnyd0muS7I+ydokvz+NYw8mef50jSdJkiRJ6h8uFjgDkhwGPAN4bFXdneShwAOn8RKDwPOBj9+fQYaGRxhctmpaApK21gYXqpQkSZK6whkBM2MRnS0A7waoqlur6r+THJzkq0nWJPl8kkUASVYneVuSbyT5ZpLHN+WDSS5NcnXzObwZ/zTg8c1Mg1cnWZDk7UmuamYgvKQrdy1JkiRJ6nkmAmbGF4BHNA/170nyh0m2B84Ejq2qg4EPAm8Z02e7qjoUeBXw903ZLcBTquqxwPOAM5ryZcClVXVQVb0TeBEwUlWHAIcAJyd55EzfpCRJkiRp7vHVgBlQVXckORh4PHAU8EngzcD+wMVJABYAN4/p9pnmew2dqf8A2wNnJTkI2AQ8aoJLPhU4IMmxzfkAsDfwvc0bJlkKLAVYsOse23B3kiRJkqS5zETADKmqTcBqYHWSIeBlwHVVddgEXe5uvjfxy7/Lq4EfAgfSmb1x1wR9A7yiqj4/hbiWA8sBdli0d235TiRJkiRJ84mJgBmQ5PeA+6rqW03RQcANwFOTHFZVlzevCjyqqq6bZKgB4Kaqui/JiXRmEQDcDuwypt3ngb9M8uWquifJo4DhqrpzsjiXLB6g7YJtkiRJktRXTATMjJ2BM5PsBtwLfJvOdPzlwBlJBuj8278LmCwR8B7g/CQvAD4HjD7Yrwc2JVkHrADeTed1gqvTee/gR8DR03xPkiRJkqR5IFXODu9XrVar2u12t8OQJEmSJE2zJGuqqjVenbsGSJIkSZLUR0wESJIkSZLUR0wEbIMkRyepJPvcj/77bkO/k5Kc1Ry/tFk7QJIkSZKkKXOxwG1zPPC15vvvt6H/0cBK4PrNK5JsV1X3bmmAqjp7G677K4aGRxhctur+DiP1nA3uhiFJkiRNyBkBWynJzsARwIuA45qyI5OsHNPmrCQnNcenJbk+yfokpyc5HHgW8PYka5PslWR1knclaQOvTPLMJFcmuSbJF5PsOU4cpyY5pTk+OclVSdYlOT/Jwhn/h5AkSZIkzUnOCNh6zwY+V1XfTPLjJAdP1DDJQ4BjgH2qqpLsVlW3JbkQWFlV5zXtAB44uqJjkt2BP2j6vBh4HfCaSWL6TFW9r+n7ZjpJijPv/61KkiRJkuYbZwRsveOBc5vjc5vziYwAdwEfSPIcYOMkbT855vjhwOeTDAGvBfbbQkz7J7m0aX/CZO2TLE3STtLetHFkC8NKkiRJkuYbEwFbIcmDgScC70+ygc5D+nOBTfzqv+WOAM27/ocC5wHPAD43yfB3jjk+EzirqpYALxkdbxIrgJc37d84WfuqWl5VrapqLVg4sIVhJUmSJEnzja8GbJ1jgY9W1UtGC5J8lU4SYN8kOwA7AU8CvtasJ7Cwqi5Kchnw3abb7cAuk1xnABhujk+cQly7ADcn2Z7OjIDhLbQHYMniAdouqiZJkiRJfcUZAVvneOCCzcrOp7No4KeAa5vva5q6XYCVSdbT2WXg/zTl5wKvbRYD3Guc65wKfDrJGuDWKcT1BuBK4DLgxinfjSRJkiSp76Squh2DuqTValW73e52GJIkSZKkaZZkzeiC9JtzRoAkSZIkSX3ERIAkSZIkSX3ERECPS/KIJF9Jcn2S65K8cpw2SXJGkm8nWZ/ksd2IVZIkSZLU+9w1oPfdC7ymqq5OsguwJsnFVXX9mDZPA/ZuPr8PvLf5ntTQ8AiDy1bNRMxSV21wNwxJkiRpQs4I6HFVdXNVXd0c3w7cACzerNmzgY9UxxXAbkkWzXKokiRJkqQ5wETAHJJkEHgMna0Cx1oMfH/M+U38erJgdIylSdpJ2ps2jsxEmJIkSZKkHmYiYI5IsjNwPvCqqvrpto5TVcurqlVVrQULB6YvQEmSJEnSnGAiYA5Isj2dJMA5VfWZcZoMA48Yc/7wpkySJEmSpF/hYoE9LkmADwA3VNU7Jmh2IfDyJOfSWSRwpKpu3tLYSxYP0HZRNUmSJEnqKyYCet/jgD8HhpKsbcr+FvgtgKo6G7gIeDrwbWAj8MIuxClJkiRJmgNMBPS4qvoakC20KeBlsxORJEmSJGkuc40ASZIkSZL6iIkASZIkSZL6iImAWZJkU5K1Yz6D47S5KMlu45SfmuSU2YhTkiRJkjS/uUbA7PlZVR00XkWzM0Cq6umzGdDQ8AiDy1bN5iWlOW+DO21IkiRpjnNGQJckGUzyH0k+AlwLPCLJhiQPber/Lsk3k3wN+L0x/U5OclWSdUnOT7IwyS5Jvpdk+6bNrmPPJUmSJEkaZSJg9uw05rWAC5qyvYH3VNV+VfWfow2THAwcBxxEZ1vAQ8aM85mqOqSqDgRuAF5UVbcDq4HRnyqPa9rds3kQSZYmaSdpb9o4Mt33KEmSJEnqcSYCZs/Pquqg5nNMU/afVXXFOG0fD1xQVRur6qfAhWPq9k9yaZIh4ARgv6b8/cALm+MXAh8aL4iqWl5VrapqLVg4cL9vSpIkSZI0t5gI6K47t6HPCuDlVbUEeCOwI0BVXQYMJjkSWFBV105XkJIkSZKk+cPFAnvTJcCKJG+l8zd6JvAvTd0uwM3N+/8nAMNj+n0E+DjwpqlcZMniAdoufCZJkiRJfcUZAT2oqq4GPgmsA/4duGpM9RuAK4HLgBs363oOsDvwiVkIU5IkSZI0B6Wquh2DpkmSY4FnV9WfT6V9q9Wqdrs9w1FJkiRJkmZbkjVV1RqvzlcD5okkZwJPo7PLgCRJkiRJ4zIRME9U1Su6HYMkSZIkqfeZCJhHkmwAbgc2AfdONA1k1NDwCIPLVs1GaNK8scEFNiVJkjTHmQiYf46qqlu7HYQkSZIkqTe5a4AkSZIkSX3ERMD8UsAXkqxJsnS8BkmWJmknaW/aODLL4UmSJEmSus1XA+aXI6pqOMlvABcnubGqLhnboKqWA8sBdli0t3tHSpIkSVKfcUbAPFJVw833LcAFwKHdjUiSJEmS1GucETBPJHkQ8ICqur05firwD5P1WbJ4gLYroEuSJElSXzERMH/sCVyQBDp/149X1ee6G5IkSZIkqdeYCJgnquq7wIHdjkOSJEmS1NtcI0CSJEmSpD5iIkCSJEmSpD7iqwE9KMlfAX8JPAx4W1WdNsV+g8DhVfXxqbQfGh5hcNmqbQ1T0jTb4OKdkiRJmgUmAnrT/waeXFU3jVeZZLuqunecqkHg+cCUEgGSJEmSpP5jIqDHJDkb+B3g35N8ENirql6eZAVwF/AY4LIknwXe3XQr4AnAacCjk6wFPlxV75z1G5AkSZIk9TQTAT2mql6a5I+Ao4BnbFb9cDpT/zcl+TfgZVV1WZKd6SQJlgGnVNXm/X4hyVJgKcCCXfeYkXuQJEmSJPUuFwucWz5dVZua48uAdzTrCew2wasCv6aqlldVq6paCxYOzFigkiRJkqTeZCJgbrlz9KBZQPDFwE50XhXYp2tRSZIkSZLmDF8NmKOS7FVVQ8BQkkOAfYDvA7tMdYwliwdou0q5JEmSJPUVZwTMXa9Kcm2S9cA9wL8D64FNSdYleXV3w5MkSZIk9aJUVbdjUJe0Wq1qt9vdDkOSJEmSNM2SrKmq1nh1zgiQJEmSJKmPmAiQJEmSJKmPuFhgj0nyMOBdwCHAbcAPgVc11e8C9gZuB74NvAJ4NPAV4FlV9W/NGCuB06tq9WTXGhoeYXDZqhm4C0m9ZoMLg0qSJKnhjIAekiTABcDqqtqrqg4G/gbYE1gFvLeq9q6qxwLvAfZout4E/F03YpYkSZIkzS0mAnrLUcA9VXX2aEFVraMzC+Dy0V/8m/LVVXVtc7oOGEnylFmNVpIkSZI055gI6C37A2u2onystwCv39IFkixN0k7S3rRxZBtClCRJkiTNZSYC5omqugQgyRFbaLe8qlpV1VqwcGB2gpMkSZIk9QwTAb3lOuDgrSjf3JRmBUiSJEmS+pe7BvSWLwP/mGRpVS0HSHIA8E3gb5L8cVWtasqfAPxkbOeq+kKSNwGLpnKxJYsHaLuSuCRJkiT1FWcE9JCqKuAY4MlJvpPkOuCtwA+AZwCvSPKtJNcD/xv40TjDvAV4xGzFLEmSJEmaW5wR0GOq6r+B505Q/UfjlP0QWD2m/4VApj8ySZIkSdJ84IwASZIkSZL6iImAKUrysCTnNlP21yS5KMmjZvB6dzTfv5nkvJm6jiRJkiSpv/hqwBQkCXAB8OGqOq4pOxDYk85CfjOmeVXg2JkYe2h4hMFlq2ZiaEk9ZoMLg0qSJKnhjICpOQq4p6rOHi2oqnXANUm+lOTqJENJng2QZDDJDUnel+S6JF9IslNTd3KSq5KsS3J+koVN+SOTXN6M8+bR6zRjXTvm+NLmelcnObwpPzLJ6iTnJbkxyTlN8kKSJEmSpF9hImBq9gfWjFN+F3BMVT2WTrLgn8Y8gO8N/HNV7QfcBvxJU/6Zqjqkqg4EbgBe1JS/G3hvVS0Bbp4gjluApzTXex5wxpi6xwCvAvYFfgd43NbfpiRJkiRpvjMRcP8E+Mck64EvAovpvC4A8L2qWtscrwEGm+P9m1/1h4ATgP2a8scBn2iOPzrB9bYH3tf0/TSdh/5R36iqm6rqPmDtmOv9asDJ0iTtJO1NG0emfqeSJEmSpHnBNQKm5jrGf0//BGAP4OCquifJBmDHpu7uMe02ATs1xyuAo6tqXZKTgCPHtKstxPFqOtsFHkgniXPXmLrNrzfu37aqlgPLAXZYtPeWridJkiRJmmdMBEzNl+n88r+0eZAmyQHAbwO3NEmAo5rzLdkFuDnJ9nQSCcNN+WXAccDHmvLxDAA3VdV9SU4EFmzzHQFLFg/QdgExSZIkSeorvhowBVVVwDHAk5vtA68D3gpcBLSaqfovAG6cwnBvAK6k8+A/tv0rgZc1Yy2eoO97gBOTrAP2Ae7clvuRJEmSJPWvdJ5x1Y9arVa12+1uhyFJkiRJmmZJ1lRVa7w6ZwRIkiRJktRHTARIkiRJktRHXCywByXZBAzR2Z5wE/By4HZ+ua3gbwEjzedW4GnAm4A/adrdDfxDVf377EYuSZIkSep1JgJ608+q6iCAJP8LeGtV/SEwWrYCWFlV5zXnpwGLgP2r6u4kewJ/uKWLDA2PMLhs1QzdgqRessEdQiRJktQwEdD7dgX+Z6LKJAuBk4FHVtXdAFX1Q+BTsxOeJEmSJGkuMRHQm3ZKshbYkc4v/U+cpO3vAv9VVT+dlcgkSZIkSXOaiYDeNPbVgMOAjyTZv6Zhr8ckS4GlAAt23eP+DidJkiRJmmPcNaDHVdXlwEOBiZ7avw38VpJdpzje8qpqVVVrwcKB6QpTkiRJkjRHOCOgxyXZB1gA/Hi8+qramOQDwLuTvKSqfp5kD+DIqvr0ZGMvWTxA2wXEJEmSJKmvmAjoTaNrBEBnC8ETq2rTJO1fD7wZuD7JXcCdwP+d4RglSZIkSXOQiYAeVFULtlB/0mbnPwde13wkSZIkSZqQawRIkiRJktRHTARIkiRJktRHTAR0UZJHJPlKkuuTXJfklU35g5NcnORbzffuTfk+SS5PcneSU8YZb0GSa5KsnO17kSRJkiTNDa4R0F33Aq+pqquT7AKsSXIxcBLwpao6LckyYBnw18BPgL8Cjp5gvFcCNwBT2kpwaHiEwWWr7uctSOp3G9x9RJIkaU5xRkAXVdXNVXV1c3w7nYf4xcCzgQ83zT5M8+BfVbdU1VXAPZuPleThwB8D75+F0CVJkiRJc5SJgB6RZBB4DHAlsGdV3dxU/QDYcwpDvIvOrgH3zUR8kiRJkqT5wURAD0iyM3A+8Kqq+unYuqoqoLbQ/xnALVW1ZgrXWpqknaQk+fLwAAAW1ElEQVS9aePI/QlbkiRJkjQHmQjosiTb00kCnFNVn2mKf5hkUVO/CLhlC8M8DnhWkg3AucATk3xsvIZVtbyqWlXVWrBwYFruQZIkSZI0d7hYYBclCfAB4IaqeseYqguBE4HTmu/PTjZOVf0N8DfNmEcCp1TVn23p+ksWD9B2kS9JkiRJ6ismArrrccCfA0NJ1jZlf0snAfCpJC8C/hN4LkCShwFtOrsC3JfkVcC+m79OIEmSJEnSREwEdFFVfQ3IBNVPGqf9D4CHb2HM1cDq+xubJEmSJGl+co0ASZIkSZL6iIkASZIkSZL6iImASSQ5Okkl2ed+9N93G/qdlOSs5vilSV6wLdeXJEmSJGlzrhEwueOBrzXff78N/Y8GVgLXb16RZLuqundLA1TV2dtw3SkZGh5hcNmqmRpeUp/Y4O4jkiRJc4ozAiaQZGfgCOBFwHFN2ZFJVo5pc1aSk5rj05Jcn2R9ktOTHA48C3h7krVJ9kqyOsm7krSBVyZ5ZpIrk1yT5ItJ9hwnjlOTnNIcn5zkqiTrkpyfZGFTviLJGUm+nuS7SY6d4X8eSZIkSdIc5YyAiT0b+FxVfTPJj5McPFHDJA8BjgH2qapKsltV3ZbkQmBlVZ3XtAN4YFW1mvPdgT9o+rwYeB3wmkli+kxVva/p+2Y6SYozm7pFdBIX+wAXAudNEOtSYCnAgl33mMq/gyRJkiRpHnFGwMSOB85tjs9tzicyAtwFfCDJc4CNk7T95JjjhwOfTzIEvBbYbwsx7Z/k0qb9CZu1/9equq+qrgd+bWbBqKpaXlWtqmotWDiwhctJkiRJkuYbEwHjSPJg4InA+5NsoPOQ/lxgE7/6b7YjQPOu/6F0foV/BvC5SYa/c8zxmcBZVbUEeMnoeJNYAby8af/GzdrfPfYWtjCOJEmSJKlP+WrA+I4FPlpVLxktSPJVOkmAfZPsAOwEPAn4WrOewMKquijJZcB3m263A7tMcp0BYLg5PnEKce0C3JxkezozAoa30H5SSxYP0HaRL0mSJEnqK84IGN/xwAWblZ1PZ9HATwHXNt/XNHW7ACuTrKezy8D/acrPBV7bLAa41zjXORX4dJI1wK1TiOsNwJXAZcCNU74bSZIkSZIaqapux6AuabVa1W63ux2GJEmSJGmaJVkzulD95pwRIEmSJElSHzERIEmSJElSHzERME2SVJKPjTnfLsmPkqxszo9McvgUx9o7ycok30myJslXkjxhpmKXJEmSJPUPdw2YPncC+yfZqap+BjyFX13V/0jgDuDrkw2SZEdgFXBKVV3YlO0PtIBLpjPgoeERBpetms4hJfWhDe4+IkmSNKc4I2B6XQSM/o/4eOATAEkGgZcCr06yNsnjJxnjBODy0SQAQFVdW1UrmrEenORfk6xPckWSA5rZB1clObJp89Ykb5nme5MkSZIkzQMmAqbXucBxza/6B9DZ6o+q2gCcDbyzqg6qqksnGWM/4OpJ6t8IXFNVBwB/C3ykqu4FTgLem+TJwB817X5NkqVJ2knamzaObNXNSZIkSZLmPhMB06iq1gODdGYDXDQdYya5IMm1ST7TFB0BfLS53peBhyTZtaqua8pXAn9RVT+fIMblVdWqqtaChQPTEaIkSZIkaQ4xETD9LgROp3ktYBtcBzx29KSqjqHza/+Dp9B3CXAb8BvbeG1JkiRJ0jznYoHT74PAbVU1NPrOfuN2YNcp9P848DdJnjVmnYCFY+ovpbOOwJua8W+tqp8meQ6dZMETgJVJDq2q2ya70JLFA7Rd5EuSJEmS+oozAqZZVd1UVWeMU/VvwDGjiwUmeVaSfxin/8+AZwAvTfLdJJcDrwfe3DQ5FTg4yXrgNODEJA9tjl9cVd8EzgLePe03J0mSJEma81JV3Y5BXdJqtardbnc7DEmSJEnSNEuypqpa49U5I0CSJEmSpD5iIkCSJEmSpD7iYoFdkOSdwH9W1bua888D36+qFzfn/wQMV9U7tmLMFcDKqjpvqn2GhkcYXLZqq2KXpM1tcNFRSZKkOcUZAd1xGXA4QJIHAA8F9htTfzjw9S7EJUmSJEma50wEdMfXgcOa4/2Aa4Hbk+yeZAfg0UAl+WqSNUk+n2QRQJK9knyuKb80yT6bD57kTUlWJFkwWzckSZIkSZobfDWgC6rqv5Pcm+S36Pz6fzmwmE5yYAS4AXgn8Oyq+lGS5wFvAf4CWA68tKq+leT3gfcATxwdO8nbgV2AF9Y4W0IkWQosBViw6x4zeJeSJEmSpF5kIqB7vk4nCXA48A46iYDD6SQChoGnAhcnAVgA3Jxk56bNp5tygB3GjPkG4MqqWjrRRatqOZ1kAjss2tu9IyVJkiSpz5gI6J7RdQKW0Hk14PvAa4CfAquBxVV12NgOSXYFbquqgyYY8yrg4CQPrqqfzFTgkiRJkqS5y0RA93wdOAX4blVtAn6SZDc6awa8BHhFkv/f3r0H2VnXdxx/f7rIJQTWUCKGYBt10jJMAkEPjtYrVJDqVGSwCrUjdqrRjrYyjq3UmVpQqdILoNaBRqvFio2IoA6gYgsOYgU8KxtDQKw06ZQYpSikpCiX+O0f+6zubHc3wF7OPue8XzM7ec7vec5zviff+c2e893f5TlV9Y0kTwB+raq2JNma5Heq6jMZGxZwZFVtau75JeDLwFVJTqiq+2cKYO3KYbqu9i1JkiRJA8XFAntnM2O7Bdw4qW1nVd0NvBI4N8kmYJRmlwHgNcAfNO1bgJMm3rSqPgN8BPhCkv3m9y1IkiRJktomU6wnpwHR6XSq2+32OgxJkiRJ0hxLMlJVnanOOSJAkiRJkqQBYiFAkiRJkqQB4mKBLZJkFXBlVa2Z0HYWsAtY05y7LMlBwL8CH6yqj093v83bd7LqzKvmNWZJGkTbXIhVkiQtYo4I6DNJhhnbOWDDTEUASZIkSdJgshDQX5YCXwQ+VVUX9joYSZIkSdLiYyGgv5wH3FBV5093QZL1SbpJursf2LmAoUmSJEmSFgMLAe0y3V6P4+3XAicledK0N6jaUFWdquoMLRme8wAlSZIkSYubhYB2+RGwbFLbQcA9zfFG4CLg6iQHLGRgkiRJkqR2cNeAFqmqXUl2JDmuqq5tdgc4EfgAcGxzzflJngxcnuRlVfXQdPdbu3KYritbS5IkSdJAcURA+7wW+PMko4xNBTi7qu6ceEFVvQO4C/inJOZYkiRJkvRzjghomaq6jeav/5PaXzfp8e8vVEySJEmSpPbwr8WSJEmSJA0QCwELLMmTk2xMcmeSkSRXN1v6XTnN9R9NcsRCxylJkiRJ6k9ODVhASQJcAVxcVac2bUcBL5/uOVX1+vmKZ/P2naw686r5ur0kDaxtLsQqSZIWMUcELKxjgYer6qLxhqraBHwNWJrksiTfSXJJUzQgyVeTdJrjXUnOSbIpyY1JDmnafzvJTUluSfIv4+2SJEmSJE1mIWBhrQFGpjl3NHAGcATwNOC5U1yzP3BjVR0FXA+8oWm/AXh2VR0NbAT+dC6DliRJkiT1D6cGLB43V9VdAM3WgKsY+4I/0UPA+FoCI8DxzfFhwKeTrAD2BrZO9yJJ1gPrAYYOXD5XsUuSJEmSWsIRAQtrC/DMac49OOF4N1MXaR6uqprimg8Bf1dVa4E3AvtOF0BVbaiqTlV1hpYMP6bgJUmSJEntZyFgYV0L7NP8VR6AJEcCz5/lfYeB7c3x6bO8lyRJkiSpjzk1YAFVVSU5GbggyTuAnwLbgM/N8tZnAZ9Jci9jxYanPponrV05TNeVrSVJkiRpoOQXI801aDqdTnW73V6HIUmSJEmaY0lGqqoz1TmnBkiSJEmSNEAsBEiSJEmSNEAsBMyBJE9OsjHJnUlGklydZH2SK6e5/qNJjljoOCVJkiRJcrHAWUoS4Arg4qo6tWk7Cnj5dM+pqtcvUHgz2rx9J6vOvKrXYUiSFoltLiArSdJAcETA7B0LPFxVF403VNUm4GvA0iSXJflOkkuaogFJvpqk0xzvSnJOkk1JbkxySNO+PMlnk3yz+Xlu0/7CJKPNzy1JDmja/6S57ttJzl7g/wNJkiRJUktYCJi9NcDINOeOBs4AjgCeBjx3imv2B26sqqOA64E3NO0fAM6vqmOAU4CPNu1vB95cVeuA5wM/SXICsBp4FrAOeGaSF8z2jUmSJEmS+o9TA+bXzVV1F0CSUWAVcMOkax4CxtcSGAGOb45fDBzRDCIAODDJUuDrwHlJLgEur6q7mkLACcAtzbVLGSsMXD85oCTrgfUAQwcun+37kyRJkiS1jIWA2dsCvHKacw9OON7N1P/fD1dVTXHNLwHPrqqfTrr+/UmuAl4KfD3JS4AA76uqv99TsFW1AdgAsM+K1bWHyyVJkiRJfcZCwOxdC/xlkvXNl2ySHMnYsP3ZuAb4I+Cvm3uuq6rRJE+vqs3A5iTHAIcDXwbek+SSqtqVZCVjBYa7Z3qBtSuH6bowlCRJkiQNFNcImKXmr/knAy9utg/cArwP+MEsb/3HQKdZ/O824E1N+xlJbk3ybeBh4ItVdQ3wKeAbSTYDlwEHzPL1JUmSJEl9KL8Yla5B0+l0qtvt9joMSZIkSdIcSzJSVZ2pzjkiQJIkSZKkAWIhQJIkSZKkAWIhYJ4k2Z1kNMmmJN9K8htN+6FJLmuO1yV56Sxe4+okT5yrmCVJkiRJ/c81AuZJkl1VtbQ5fgnwzqp64aRrXgd0quotPQiRfVasrhWnX9CLl5YkSS21zR2HJKkVXCOg9w4E7gVIsqpZ9X9v4N3Aq5uRA69OsjTJx5NsbnYLOKV5zmlN261Jzh2/aZJtSQ5u7nl7ko8k2ZLkmiT79eSdSpIkSZIWtb16HUAf2y/JKLAvsAI4buLJqnooybuYMCKg+ZK/s6rWNo+XJTkUOBd4JmPFhGuSvKKqPjfp9VYDp1XVG5JcCpwCfHIe358kSZIkqYUcETB/flJV66rqcOBE4BNJsofnvBj48PiDqroXOAb4alX9d1U9AlwCvGCK526tqtHmeARYNdULJFmfpJuku/uBnY/tHUmSJEmSWs9CwAKoqm8ABwPL5/FlHpxwvJtpRntU1Yaq6lRVZ2jJ8DyGI0mSJElajJwasACSHA4MAT8Clkw4dT9wwITHXwHeDJzRPG8ZcDPwwSQHMzY14DTgQ3MR19qVw3Rd8EeSJEmSBoojAubPfs0igKPAp4HTq2r3pGuuA44YXywQeC+wrFkUcBNwbFXtAM5srt0EjFTV5xfwfUiSJEmS+ojbBw6wTqdT3W6312FIkiRJkuaY2wdKkiRJkiTAQoAkSZIkSQPFQkCLJFmV5NZJbWcleXuSZye5qVlv4PYkZ/UoTEmSJEnSIuauAf3jYuBVVbUpyRDw63t6wubtO1l15lXzH5kkSZKkVtrmLmN9yUJA/3gSsAOg2Z3gtt6GI0mSJElajJwa0D/OB+5IckWSNybZd6qLkqxP0k3S3f3AzgUOUZIkSZLUaxYC2mW6vR6rqt4NdIBrgN8FvjTNhRuqqlNVnaElw/MUpiRJkiRpsbIQ0C4/ApZNajsIuAegqu6sqguB3wSOSvLLCxyfJEmSJGmRc42AFqmqXUl2JDmuqq5NchBwIvCBJC8Drq6qAlYDu4H7Zrrf2pXDdF38Q5IkSZIGioWA9nkt8OEk5zWPz66qO5OcA5yf5AHgEeA1zaKBkiRJkiT9nIWAlqmq24Bjp2g/tQfhSJIkSZJaxjUCJEmSJEkaIBYCJEmSJEkaIBYCFpEku5OMTvhZleTfHuM9zkiyZL5ilCRJkiS1W8YWmddikGRXVS19FNftVVWPTHNuG9Cpqnv2dJ99VqyuFadf8NgDlSRJkqQBta0lO68lGamqzlTnXCxwkRsvDiR5EfAe4F7g8CRHA5cChwFDzblDgEOB65LcU1X/b1FBSZIkSdJgsxCwuOyXZLQ53lpVJ086/wxgTVVtTXIK8P2qehlAkuGq2pnkbcCx040ISLIeWA8wdODy+XkXkiRJkqRFyzUCFpefVNW65mdyEQDg5qra2hxvBo5Pcm6S51fVzkfzAlW1oao6VdUZWjI8Z4FLkiRJktrBQkC7/O/4QVV9l7ERApuB9yZ5V8+ikiRJkiS1hlMDWirJocCPq+qTSe4DXt+cuh84ANjjYoFrVw7TbclCF5IkSZKkuWEhoL3WAn+d5GfAw8AfNu0bgC8l+b6LBUqSJEmSJnP7wAHW6XSq2+32OgxJkiRJ0hybaftACwEDLMn9wB29jkOzdjCPYiqIWsFc9g9z2T/MZf8wl/3DXPYPczm/frWqptwqzqkBg+2O6SpEao8kXfPYH8xl/zCX/cNc9g9z2T/MZf8wl73jrgGSJEmSJA0QCwGSJEmSJA0QCwGDbUOvA9CcMI/9w1z2D3PZP8xl/zCX/cNc9g9z2SMuFihJkiRJ0gBxRIAkSZIkSQPEQsAASnJikjuSfC/Jmb2OR49fkm1JNicZTdLtdTx69JJ8LMndSW6d0HZQkq8k+ffm32W9jFGPzjS5PCvJ9qZvjiZ5aS9j1KOT5ClJrktyW5ItSd7atNs3W2SGPNovWybJvkluTrKpyeXZTftTk9zUfJb9dJK9ex2rZjZDLv8xydYJ/XJdr2MdFE4NGDBJhoDvAscDdwHfBE6rqtt6GpgelyTbgE5Vuf9qyyR5AbAL+ERVrWna/gr4cVW9vynSLauqd/QyTu3ZNLk8C9hVVX/Ty9j02CRZAayoqm8lOQAYAV4BvA77ZmvMkMdXYb9slSQB9q+qXUmeANwAvBV4G3B5VW1MchGwqaou7GWsmtkMuXwTcGVVXdbTAAeQIwIGz7OA71XVf1TVQ8BG4KQexyQNnKq6HvjxpOaTgIub44sZ++CqRW6aXKqFqmpHVX2rOb4fuB1YiX2zVWbIo1qmxuxqHj6h+SngOGD8i6N9sgVmyKV6xELA4FkJ/NeEx3fhL8c2K+CaJCNJ1vc6GM3aIVW1ozn+AXBIL4PRrL0lybebqQMOJW+ZJKuAo4GbsG+21qQ8gv2ydZIMJRkF7ga+AtwJ3FdVjzSX+Fm2JSbnsqrG++U5Tb88P8k+PQxxoFgIkNrteVX1DOC3gDc3Q5TVB2ps3paV8va6EHg6sA7YAfxtb8PRY5FkKfBZ4Iyq+p+J5+yb7TFFHu2XLVRVu6tqHXAYYyNbD+9xSHqcJucyyRrgzxjL6THAQYDTrhaIhYDBsx14yoTHhzVtaqGq2t78ezdwBWO/INVeP2zmto7Pcb27x/HocaqqHzYfeH4GfAT7Zms0c1c/C1xSVZc3zfbNlpkqj/bLdquq+4DrgOcAT0yyV3PKz7ItMyGXJzZTeaqqHgQ+jv1ywVgIGDzfBFY3q63uDZwKfKHHMelxSLJ/swgSSfYHTgBunflZWuS+AJzeHJ8OfL6HsWgWxr80Nk7GvtkKzWJW/wDcXlXnTThl32yR6fJov2yfJMuTPLE53o+xxa5vZ+xL5Cuby+yTLTBNLr8zocgaxtZ6sF8uEHcNGEDNdjkXAEPAx6rqnB6HpMchydMYGwUAsBfwKXPZHkn+GXgRcDDwQ+AvgM8BlwK/Avwn8KqqchG6RW6aXL6IseHHBWwD3jhhjrkWqSTPA74GbAZ+1jS/k7H55fbNlpghj6dhv2yVJEcythjgEGN/wLy0qt7dfAbayNhQ8luA32v+oqxFaoZcXgssBwKMAm+asKig5pGFAEmSJEmSBohTAyRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiAWAiRJkiRJGiD/BwTCuOPvCc1fAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}],"source":["ner_df.entities_ner.value_counts()[:100].plot.barh(figsize = (16,20))"]},{"cell_type":"markdown","metadata":{"id":"ElYjed34Bu8T"},"source":["### Top 50 Named Entities which are PERSONS"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"rynx78HUvQJI","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1650026831311,"user_tz":-300,"elapsed":3398,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"4d160f60-df12-4daa-9d06-ec9b37302613"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f2233359150>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x1440 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["ner_df[ner_df.entities_ner_class == 'PERSON'].entities_ner.value_counts()[:50].plot.barh(figsize=(18,20), title ='Top 50 Occuring Persons in the dataset')"]},{"cell_type":"markdown","metadata":{"id":"B8oAF_MCBxyN"},"source":["### Top 50 Named Entities which are Countries/Cities/States"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"IEiIMsFj9v2n","executionInfo":{"status":"ok","timestamp":1650026831315,"user_tz":-300,"elapsed":33,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"6deca13a-b004-4ae3-c1c9-ca5c1b1e7cad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f222c9a7090>"]},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x1440 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["ner_df[ner_df.entities_ner_class == 'GPE'].entities_ner.value_counts()[:50].plot.barh(figsize=(18,20),title ='Top 50 Countries/Cities/States Occuring in the dataset')"]},{"cell_type":"markdown","metadata":{"id":"JiHofegqB0sM"},"source":["### Top 50 Named Entities which are PRODUCTS "]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"coeW-Kgs92fH","executionInfo":{"status":"ok","timestamp":1650026832313,"user_tz":-300,"elapsed":1023,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"4cc7431f-115e-42fa-d356-38c0e5b18300"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f222c9a7750>"]},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x1440 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["ner_df[ner_df.entities_ner_class == 'PRODUCT'].entities_ner.value_counts()[:50].plot.barh(figsize=(18,20),title ='Top 50 products occuring in the dataset')"]},{"cell_type":"markdown","metadata":{"id":"ybfGwL9-B7MN"},"source":["### Top 50 Named Entities which are ORGANIZATIONS"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"4ng-TNYu--09","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1650026833103,"user_tz":-300,"elapsed":801,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"04decf3b-cf4b-4bcd-bbb7-5c34a39a70e4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f222d3f1850>"]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x1440 with 1 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\n"},"metadata":{"needs_background":"light"}}],"source":["ner_df[ner_df.entities_ner_class == 'ORG'].entities_ner.value_counts()[:50].plot.barh(figsize=(18,20),title ='Top 50 products occuring in the dataset')"]},{"cell_type":"markdown","metadata":{"id":"p73T7MyeU2kS"},"source":["## YAKE on a Crypto News dataset\n","### The **YAKE!** model (Yet Another Keyword Extractor) is a **unsupervised** keyword extraction algorithm.\n","You can load it via   which you can load via `nlu.load('yake')`. It has no weights and is very fast.\n","It has various parameters that can be configured to influence which keywords are beeing extracted, [here for an more indepth YAKE guide](https://github.com/JohnSnowLabs/nlu/blob/master/examples/webinars_conferences_etc/multi_lingual_webinar/1_NLU_base_features_on_dataset_with_YAKE_Lemma_Stemm_classifiers_NER_.ipynb)"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"Zu8-yar9VLqO","colab":{"base_uri":"https://localhost:8080/","height":476},"executionInfo":{"status":"ok","timestamp":1650026846991,"user_tz":-300,"elapsed":13898,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"2ced552b-7455-4d1d-e6e1-e1264875aa34"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                               document           keywords  \\\n","0        Bitcoin Price Update: Will China Lead us Down?             update   \n","0        Bitcoin Price Update: Will China Lead us Down?              china   \n","0        Bitcoin Price Update: Will China Lead us Down?         china lead   \n","1     Key Bitcoin Price Levels for Week 51 (15 – 22 ...              price   \n","1     Key Bitcoin Price Levels for Week 51 (15 – 22 ...             levels   \n","...                                                 ...                ...   \n","1998  Cryptocurrency Exchange Platform AlphaPoint Pa...             growth   \n","1998  Cryptocurrency Exchange Platform AlphaPoint Pa...     support growth   \n","1999  Want to Buy And Sell Bitcoin Fast and Secure? ...       bitcoin fast   \n","1999  Want to Buy And Sell Bitcoin Fast and Secure? ...        try coinrnr   \n","1999  Want to Buy And Sell Bitcoin Fast and Secure? ...  sell bitcoin fast   \n","\n","      keywords_confidence  \n","0      0.5798862558280943  \n","0      0.5798862558280943  \n","0      0.5066323531331214  \n","1      0.5798862558280943  \n","1      0.5798862558280943  \n","...                   ...  \n","1998  0.26804494089513314  \n","1998   0.1840422979793308  \n","1999   0.3579604335906263  \n","1999   0.2564243599387429  \n","1999  0.28203029979078753  \n","\n","[6085 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-20444338-0d8a-4093-80fa-1fd12e1921b8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>keywords</th>\n","      <th>keywords_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>update</td>\n","      <td>0.5798862558280943</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>china</td>\n","      <td>0.5798862558280943</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>china lead</td>\n","      <td>0.5066323531331214</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>price</td>\n","      <td>0.5798862558280943</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>levels</td>\n","      <td>0.5798862558280943</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>Cryptocurrency Exchange Platform AlphaPoint Pa...</td>\n","      <td>growth</td>\n","      <td>0.26804494089513314</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>Cryptocurrency Exchange Platform AlphaPoint Pa...</td>\n","      <td>support growth</td>\n","      <td>0.1840422979793308</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure? ...</td>\n","      <td>bitcoin fast</td>\n","      <td>0.3579604335906263</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure? ...</td>\n","      <td>try coinrnr</td>\n","      <td>0.2564243599387429</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure? ...</td>\n","      <td>sell bitcoin fast</td>\n","      <td>0.28203029979078753</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>6085 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20444338-0d8a-4093-80fa-1fd12e1921b8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-20444338-0d8a-4093-80fa-1fd12e1921b8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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YAKE!"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"CMXkeiCLVo4u","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1650026848055,"user_tz":-300,"elapsed":1078,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"b7b1fd82-434b-45a0-ed8b-7212cf5039b8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f223237b190>"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["<Figure size 1008x1296 with 1 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\n"},"metadata":{"needs_background":"light"}}],"source":["yake_df.explode('keywords').keywords.value_counts()[0:50].plot.barh(figsize=(14,18))"]},{"cell_type":"markdown","metadata":{"id":"gXoYzE_RBaaj"},"source":["## Binary Sentimental Analysis and Distribution on a dataset"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"yjHx4TR3_SMe","colab":{"base_uri":"https://localhost:8080/","height":580},"executionInfo":{"status":"ok","timestamp":1650026874405,"user_tz":-300,"elapsed":26363,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"a436cb4f-79df-4c5a-8fe4-91321374b6d6"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentimentdl_glove_imdb download started this may take some time.\n","Approximate size to download 8.7 MB\n","[OK!]\n","glove_100d download started this may take some time.\n","Approximate size to download 145.3 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                               sentence  \\\n","0        Bitcoin Price Update: Will China Lead us Down?   \n","1     Key Bitcoin Price Levels for Week 51 (15 – 22 ...   \n","2     National Australia Bank, Citing Highly Flawed ...   \n","3     Chinese Bitcoin Ban Driven by Chinese Banking ...   \n","4                 Bitcoin Trade Update: Opened Position   \n","...                                                 ...   \n","1996  NYDFS Extends BitLicense Bitcoin Regulation Co...   \n","1997  Bitfinex Passes Stefan Thomas’s Proof Of Solve...   \n","1998  Cryptocurrency Exchange Platform AlphaPoint Pa...   \n","1999      Want to Buy And Sell Bitcoin Fast and Secure?   \n","1999                                        Try CoinRNR   \n","\n","        sentence_embedding_sentence_embedding_converter sentiment  \\\n","0     [0.03502599149942398, 0.3820391297340393, 0.76...       neg   \n","1     [0.04941307753324509, 0.1195848360657692, 0.17...       neg   \n","2     [-0.1178220584988594, 0.02187376841902733, 0.3...       neg   \n","3     [0.19560889899730682, 0.19520443677902222, 0.3...       neg   \n","4     [0.048274993896484375, 0.14680083096027374, 0....       pos   \n","...                                                 ...       ...   \n","1996  [0.1282588541507721, -0.1378742903470993, 0.17...       neg   \n","1997  [-0.22165587544441223, -0.19953998923301697, 0...       neg   \n","1998  [0.027292732149362564, 0.3372064232826233, -0....       pos   \n","1999  [0.0892653837800026, 0.25083836913108826, 0.14...       pos   \n","1999  [-0.16664999723434448, -0.07406999915838242, -...       pos   \n","\n","     sentiment_confidence                               word_embedding_glove  \n","0                0.899232  [[0.8403199911117554, 0.13267000019550323, -0....  \n","1                0.994394  [[-0.22009000182151794, 0.12280000001192093, 0...  \n","2                0.998585  [[-0.003313800087198615, 0.3894599974155426, 0...  \n","3                0.999998  [[0.4327400028705597, 0.3958199918270111, 0.58...  \n","4                0.985043  [[0.8403199911117554, 0.13267000019550323, -0....  \n","...                   ...                                                ...  \n","1996             0.998673  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1997             0.999471  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1998             0.999911  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n","1999             0.973229  [[-0.17124000191688538, 0.5644699931144714, 0....  \n","1999             0.973229  [[-0.17124000191688538, 0.5644699931144714, 0....  \n","\n","[2160 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-3946fb77-4564-44fa-8b23-24c0b0d2e9e8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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<td>National Australia Bank, Citing Highly Flawed ...</td>\n","      <td>[-0.1178220584988594, 0.02187376841902733, 0.3...</td>\n","      <td>neg</td>\n","      <td>0.998585</td>\n","      <td>[[-0.003313800087198615, 0.3894599974155426, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Chinese Bitcoin Ban Driven by Chinese Banking ...</td>\n","      <td>[0.19560889899730682, 0.19520443677902222, 0.3...</td>\n","      <td>neg</td>\n","      <td>0.999998</td>\n","      <td>[[0.4327400028705597, 0.3958199918270111, 0.58...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bitcoin Trade Update: Opened Position</td>\n","      <td>[0.048274993896484375, 0.14680083096027374, 0....</td>\n","      <td>pos</td>\n","      <td>0.985043</td>\n","      <td>[[0.8403199911117554, 0.13267000019550323, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1996</th>\n","      <td>NYDFS Extends BitLicense Bitcoin Regulation Co...</td>\n","      <td>[0.1282588541507721, -0.1378742903470993, 0.17...</td>\n","      <td>neg</td>\n","      <td>0.998673</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1997</th>\n","      <td>Bitfinex Passes Stefan Thomas’s Proof Of Solve...</td>\n","      <td>[-0.22165587544441223, -0.19953998923301697, 0...</td>\n","      <td>neg</td>\n","      <td>0.999471</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>Cryptocurrency Exchange Platform AlphaPoint Pa...</td>\n","      <td>[0.027292732149362564, 0.3372064232826233, -0....</td>\n","      <td>pos</td>\n","      <td>0.999911</td>\n","      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure?</td>\n","      <td>[0.0892653837800026, 0.25083836913108826, 0.14...</td>\n","      <td>pos</td>\n","      <td>0.973229</td>\n","      <td>[[-0.17124000191688538, 0.5644699931144714, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Try CoinRNR</td>\n","      <td>[-0.16664999723434448, -0.07406999915838242, -...</td>\n","      <td>pos</td>\n","      <td>0.973229</td>\n","      <td>[[-0.17124000191688538, 0.5644699931144714, 0....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2160 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3946fb77-4564-44fa-8b23-24c0b0d2e9e8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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')"]},{"cell_type":"markdown","metadata":{"id":"3a3xxhUSCDhJ"},"source":["## Emotional Analysis and Distribution of Headlines "]},{"cell_type":"code","execution_count":18,"metadata":{"id":"rrYi4f1PEpV3","colab":{"base_uri":"https://localhost:8080/","height":580},"executionInfo":{"status":"ok","timestamp":1650026955765,"user_tz":-300,"elapsed":81377,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"012007fa-2d09-4201-d086-c16f639fe8a4"},"outputs":[{"output_type":"stream","name":"stdout","text":["classifierdl_use_emotion download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["     emotion emotion_confidence_confidence  \\\n","0       fear                      0.998173   \n","1        joy                      0.997696   \n","2       fear                      0.999997   \n","3       fear                      0.999135   \n","4        joy                      0.998864   \n","...      ...                           ...   \n","1996    fear                      0.998281   \n","1997    fear                      0.772052   \n","1998     joy                      0.999348   \n","1999    fear                      0.998905   \n","1999    fear                      0.998905   \n","\n","                                               sentence  \\\n","0        Bitcoin Price Update: Will China Lead us Down?   \n","1     Key Bitcoin Price Levels for Week 51 (15 – 22 ...   \n","2     National Australia Bank, Citing Highly Flawed ...   \n","3     Chinese Bitcoin Ban Driven by Chinese Banking ...   \n","4                 Bitcoin Trade Update: Opened Position   \n","...                                                 ...   \n","1996  NYDFS Extends BitLicense Bitcoin Regulation Co...   \n","1997  Bitfinex Passes Stefan Thomas’s Proof Of Solve...   \n","1998  Cryptocurrency Exchange Platform AlphaPoint Pa...   \n","1999      Want to Buy And Sell Bitcoin Fast and Secure?   \n","1999                                        Try CoinRNR   \n","\n","                                 sentence_embedding_use  \n","0     [0.05829371139407158, -0.036904484033584595, -...  \n","1     [0.038088250905275345, -0.04514157399535179, -...  \n","2     [0.05034318566322327, -0.01303655095398426, -0...  \n","3     [0.055152829736471176, -0.05237917602062225, -...  \n","4     [0.05926975607872009, -0.056463420391082764, -...  \n","...                                                 ...  \n","1996  [0.0639236643910408, -0.05505230277776718, -0....  \n","1997  [0.059178080409765244, -0.041498005390167236, ...  \n","1998  [0.05369672179222107, -0.023480931296944618, -...  \n","1999  [0.0626637190580368, -0.05945301055908203, -0....  \n","1999  [0.02854502573609352, 0.05557611957192421, 0.0...  \n","\n","[2160 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-96754f32-7fed-4f56-b1ed-a1ac4646dac5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>emotion</th>\n","      <th>emotion_confidence_confidence</th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_use</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>fear</td>\n","      <td>0.998173</td>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>[0.05829371139407158, -0.036904484033584595, -...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>joy</td>\n","      <td>0.997696</td>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>[0.038088250905275345, -0.04514157399535179, -...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>fear</td>\n","      <td>0.999997</td>\n","      <td>National Australia Bank, Citing Highly Flawed ...</td>\n","      <td>[0.05034318566322327, -0.01303655095398426, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>fear</td>\n","      <td>0.999135</td>\n","      <td>Chinese Bitcoin Ban Driven by Chinese Banking ...</td>\n","      <td>[0.055152829736471176, -0.05237917602062225, -...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>joy</td>\n","      <td>0.998864</td>\n","      <td>Bitcoin Trade Update: Opened Position</td>\n","      <td>[0.05926975607872009, -0.056463420391082764, -...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1996</th>\n","      <td>fear</td>\n","      <td>0.998281</td>\n","      <td>NYDFS Extends BitLicense Bitcoin Regulation Co...</td>\n","      <td>[0.0639236643910408, -0.05505230277776718, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>1997</th>\n","      <td>fear</td>\n","      <td>0.772052</td>\n","      <td>Bitfinex Passes Stefan Thomas’s Proof Of Solve...</td>\n","      <td>[0.059178080409765244, -0.041498005390167236, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>joy</td>\n","      <td>0.999348</td>\n","      <td>Cryptocurrency Exchange Platform AlphaPoint Pa...</td>\n","      <td>[0.05369672179222107, -0.023480931296944618, -...</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>fear</td>\n","      <td>0.998905</td>\n","      <td>Want to Buy And Sell Bitcoin Fast and Secure?</td>\n","      <td>[0.0626637190580368, -0.05945301055908203, -0....</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>fear</td>\n","      <td>0.998905</td>\n","      <td>Try CoinRNR</td>\n","      <td>[0.02854502573609352, 0.05557611957192421, 0.0...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2160 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96754f32-7fed-4f56-b1ed-a1ac4646dac5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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\n"},"metadata":{"needs_background":"light"}}],"source":["emo_df.emotion.value_counts().plot.bar(title='Emotion Distribution')\n"]},{"cell_type":"markdown","metadata":{"id":"a7wmKnjfiMjo"},"source":["**Make sure to restart your notebook again** before starting the next section"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"BHD2-AnQsYbX","colab":{"base_uri":"https://localhost:8080/","height":200},"executionInfo":{"status":"error","timestamp":1650026956423,"user_tz":-300,"elapsed":702,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"7820abbc-cc63-4c74-8d13-de171be72665"},"outputs":[{"output_type":"stream","name":"stdout","text":["Please restart kernel if you are in google colab to free up RAM\n"]},{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-20-eb226dd89e38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please restart kernel if you are in google colab to free up RAM\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'wait'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"]}],"source":["print(\"Please restart kernel if you are in google colab to free up RAM\")\n","1+'wait'\n"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"-EMQEjLaXFlE","executionInfo":{"status":"ok","timestamp":1650027044633,"user_tz":-300,"elapsed":2983,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}}},"outputs":[],"source":["import nlu \n","import pandas as pd "]},{"cell_type":"markdown","metadata":{"id":"rN_H9hKmApll"},"source":["# Answer **Closed Book** and Open **Book Questions** with Google's T5!\n","\n","<!-- [T5]() -->\n","![T5 GIF](https://1.bp.blogspot.com/-o4oiOExxq1s/Xk26XPC3haI/AAAAAAAAFU8/NBlvOWB84L0PTYy9TzZBaLf6fwPGJTR0QCLcBGAsYHQ/s1600/image3.gif)\n","\n","You can load the **question answering** model with `nlu.load('en.t5')`"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sKmud8AHN9yo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027094024,"user_tz":-300,"elapsed":49395,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"e0b04310-68ba-4c5d-8bbe-2b6871782c36"},"outputs":[{"output_type":"stream","name":"stdout","text":["google_t5_small_ssm_nq download started this may take some time.\n","Approximate size to download 139 MB\n","[OK!]\n"]}],"source":["# Load question answering T5 model\n","t5_closed_question = nlu.load('en.t5')"]},{"cell_type":"markdown","metadata":{"id":"-F9rrWbfNyPZ"},"source":["## Answer **Closed Book Questions**  \n","Closed book means that no additional context is given and the model must answer the question with the knowledge stored in it's weights"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"QsvnphOwfzVQ","colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1650027101801,"user_tz":-300,"elapsed":7817,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"ab081202-cde1-4e6b-ffff-3af49beb62ec"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       document                t5\n","0  Who is president of Nigeria?  Muhammadu Buhari"],"text/html":["\n","  <div id=\"df-6db870ba-fc81-44dd-8d2d-3cc9fa917bed\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Who is president of Nigeria?</td>\n","      <td>Muhammadu Buhari</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6db870ba-fc81-44dd-8d2d-3cc9fa917bed')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-6db870ba-fc81-44dd-8d2d-3cc9fa917bed button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":3}],"source":["t5_closed_question.predict(\"Who is president of Nigeria?\")"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"DcTbqAGmM6YY","colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1650027102552,"user_tz":-300,"elapsed":758,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"df27649d-5311-43d1-9269-e7004f97d7cd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                     document     t5\n","0  What is the most common language in India?  Hindi"],"text/html":["\n","  <div id=\"df-e293e4db-880d-41f3-9a0d-6d58d3b82e61\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>What is the most common language in India?</td>\n","      <td>Hindi</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e293e4db-880d-41f3-9a0d-6d58d3b82e61')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e293e4db-880d-41f3-9a0d-6d58d3b82e61 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":4}],"source":["t5_closed_question.predict(\"What is the most common language in India?\")"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"2Rb4EhK_NAb3","colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1650027103191,"user_tz":-300,"elapsed":649,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"d5fcd37c-4810-4555-fd04-cc413d7b8194"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          document      t5\n","0  What is the capital of Germany?  Berlin"],"text/html":["\n","  <div id=\"df-069d3926-1874-4dde-b657-80bbdd2d33f0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>What is the capital of Germany?</td>\n","      <td>Berlin</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-069d3926-1874-4dde-b657-80bbdd2d33f0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-069d3926-1874-4dde-b657-80bbdd2d33f0 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":5}],"source":["t5_closed_question.predict(\"What is the capital of Germany?\")"]},{"cell_type":"markdown","metadata":{"id":"ogxJNa5MOOQj"},"source":["## Answer **Open Book Questions** \n","These are questions where we give the model some additional context, that is used to answer the question"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"e9cwqQGtaTa5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027166950,"user_tz":-300,"elapsed":63776,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"c28b60f2-9193-4dfb-f191-efbf5d33c002"},"outputs":[{"output_type":"stream","name":"stdout","text":["t5_base download started this may take some time.\n","Approximate size to download 446 MB\n","[OK!]\n"]}],"source":["t5_open_book = nlu.load('answer_question')"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"OB5GOHxPYUYM","colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"status":"ok","timestamp":1650027173860,"user_tz":-300,"elapsed":6915,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"3ec3e574-7033-4183-d1eb-e963af36767e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                            document  \\\n","0  Why was peters week so bad?Peters last week wa...   \n","1  How did peter broke his leg?Peters last week w...   \n","\n","                                                  t5  \n","0  He had an accident and broke his leg while skiing  \n","1                                             skiing  "],"text/html":["\n","  <div id=\"df-467a682d-d9b6-425f-9017-d273839db999\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Why was peters week so bad?Peters last week wa...</td>\n","      <td>He had an accident and broke his leg while skiing</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>How did peter broke his leg?Peters last week w...</td>\n","      <td>skiing</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-467a682d-d9b6-425f-9017-d273839db999')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":7}],"source":["context   = 'Peters last week was terrible! He had an accident and broke his leg while skiing!'\n","question1  = 'Why was peters week so bad?' \n","question2  = 'How did peter broke his leg?' \n","\n","t5_open_book.predict([question1+context, question2 + context]) "]},{"cell_type":"code","execution_count":8,"metadata":{"id":"kZb_BdGm1-yc","executionInfo":{"status":"ok","timestamp":1650027173861,"user_tz":-300,"elapsed":6,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}}},"outputs":[],"source":["# Ask T5 questions in the context of a News Article\n","question1 = 'Who is Jack ma?'\n","question2 = 'Who is founder of Alibaba Group?'\n","question3 = 'When did Jack Ma re-appear?'\n","question4 = 'How did Alibaba stocks react?'\n","question5 = 'Whom did Jack Ma meet?'\n","question6 = 'Who did Jack Ma hide from?'\n","\n","\n","# from https://www.bbc.com/news/business-55728338 \n","news_article_context = \"\"\" context:\n","Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire.\n","His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses.\n","The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media.\n","Alibaba shares surged 5% on Hong Kong's stock exchange on the news.\n","\"\"\"\n","\n","questions = [\n","             question1+ news_article_context,\n","             question2+ news_article_context,\n","             question3+ news_article_context,\n","             question4+ news_article_context,\n","             question5+ news_article_context,\n","             question6+ news_article_context,]\n","\n"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"e0kTj4ZN4kJi","colab":{"base_uri":"https://localhost:8080/","height":238},"executionInfo":{"status":"ok","timestamp":1650027192867,"user_tz":-300,"elapsed":19011,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"8425d276-80e6-4588-8f8c-bf630ce127e7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                            document                     t5\n","0  Who is Jack ma? context: Alibaba Group founder...  Alibaba Group founder\n","1  Who is founder of Alibaba Group? context: Alib...                Jack Ma\n","2  When did Jack Ma re-appear? context: Alibaba G...              Wednesday\n","3  How did Alibaba stocks react? context: Alibaba...              surged 5%\n","4  Whom did Jack Ma meet? context: Alibaba Group ...     100 rural teachers\n","5  Who did Jack Ma hide from? context: Alibaba Gr...     Chinese regulators"],"text/html":["\n","  <div id=\"df-c8d8d4d8-a3c7-48bd-83a5-c3cca6bbee54\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Who is Jack ma? context: Alibaba Group founder...</td>\n","      <td>Alibaba Group founder</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Who is founder of Alibaba Group? context: Alib...</td>\n","      <td>Jack Ma</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>When did Jack Ma re-appear? context: Alibaba G...</td>\n","      <td>Wednesday</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>How did Alibaba stocks react? context: Alibaba...</td>\n","      <td>surged 5%</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Whom did Jack Ma meet? context: Alibaba Group ...</td>\n","      <td>100 rural teachers</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Who did Jack Ma hide from? context: Alibaba Gr...</td>\n","      <td>Chinese regulators</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8d8d4d8-a3c7-48bd-83a5-c3cca6bbee54')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c8d8d4d8-a3c7-48bd-83a5-c3cca6bbee54 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":9}],"source":["t5_open_book.predict(questions)"]},{"cell_type":"markdown","metadata":{"id":"xJIuT3ZhOhoc"},"source":["# Multi Problem T5 model for Summarization and more\n","The main T5 model was trained for over 20 tasks from the SQUAD/GLUE/SUPERGLUE datasets. See [this notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/webinars_conferences_etc/multi_lingual_webinar/7_T5_SQUAD_GLUE_SUPER_GLUE_TASKS.ipynb) for a demo of all tasks \n","\n","\n","# Overview of every task available with T5\n","[The T5 model](https://arxiv.org/pdf/1910.10683.pdf) is trained on various datasets for 17 different tasks which fall into 8 categories.\n","\n","\n","\n","1. Text summarization\n","2. Question answering\n","3. Translation\n","4. Sentiment analysis\n","5. Natural Language inference\n","6. Coreference resolution\n","7. Sentence Completion\n","8. Word sense disambiguation\n","\n","### Every T5 Task with explanation:\n","|Task Name | Explanation | \n","|----------|--------------|\n","|[1.CoLA](https://nyu-mll.github.io/CoLA/)                   | Classify if a sentence is gramaticaly correct|\n","|[2.RTE](https://dl.acm.org/doi/10.1007/11736790_9)                    | Classify whether if a statement can be deducted from a sentence|\n","|[3.MNLI](https://arxiv.org/abs/1704.05426)                   | Classify for a hypothesis and premise whether they contradict or contradict each other or neither of both (3 class).|\n","|[4.MRPC](https://www.aclweb.org/anthology/I05-5002.pdf)                   | Classify whether a pair of sentences is a re-phrasing of each other (semantically equivalent)|\n","|[5.QNLI](https://arxiv.org/pdf/1804.07461.pdf)                   | Classify whether the answer to a question can be deducted from an answer candidate.|\n","|[6.QQP](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)                    | Classify whether a pair of questions is a re-phrasing of each other (semantically equivalent)|\n","|[7.SST2](https://www.aclweb.org/anthology/D13-1170.pdf)                   | Classify the sentiment of a sentence as positive or negative|\n","|[8.STSB](https://www.aclweb.org/anthology/S17-2001/)                   | Classify the sentiment of a sentence on a scale from 1 to 5 (21 Sentiment classes)|\n","|[9.CB](https://ojs.ub.uni-konstanz.de/sub/index.php/sub/article/view/601)                     | Classify for a premise and a hypothesis whether they contradict each other or not (binary).|\n","|[10.COPA](https://www.aaai.org/ocs/index.php/SSS/SSS11/paper/view/2418/0)                   | Classify for a question, premise, and 2 choices which choice the correct choice is (binary).|\n","|[11.MultiRc](https://www.aclweb.org/anthology/N18-1023.pdf)                | Classify for a question, a paragraph of text, and an answer candidate, if the answer is correct (binary),|\n","|[12.WiC](https://arxiv.org/abs/1808.09121)                    | Classify for a pair of sentences and a disambigous word if the word has the same meaning in both sentences.|\n","|[13.WSC/DPR](https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492/0)       | Predict for an ambiguous pronoun in a sentence what it is referring to.  |\n","|[14.Summarization](https://arxiv.org/abs/1506.03340)          | Summarize text into a shorter representation.|\n","|[15.SQuAD](https://arxiv.org/abs/1606.05250)                  | Answer a question for a given context.|\n","|[16.WMT1.](https://arxiv.org/abs/1706.03762)                  | Translate English to German|\n","|[17.WMT2.](https://arxiv.org/abs/1706.03762)                   | Translate English to French|\n","|[18.WMT3.](https://arxiv.org/abs/1706.03762)                   | Translate English to Romanian|\n","\n"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"XJw187r91QKN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027197162,"user_tz":-300,"elapsed":4306,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"677cf136-08c0-4a43-9e79-259b05b67ca6"},"outputs":[{"output_type":"stream","name":"stdout","text":["t5_base download started this may take some time.\n","Approximate size to download 446 MB\n","[OK!]\n"]}],"source":["# Load the Multi Task Model T5\n","t5_multi = nlu.load('en.t5.base')"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"_F6jE7IN1U-G","colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1650027235090,"user_tz":-300,"elapsed":25694,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"407b4892-cc1a-477a-f61c-334e1aea4d9b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                            document  \\\n","0  (Reuters) - Mastercard Inc said on Wednesday i...   \n","\n","                                                  t5  \n","0  mastercard said on Wednesday it was planning t...  "],"text/html":["\n","  <div id=\"df-07f94fb3-020d-4925-8368-e75b109228d2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>t5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>(Reuters) - Mastercard Inc said on Wednesday i...</td>\n","      <td>mastercard said on Wednesday it was planning t...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07f94fb3-020d-4925-8368-e75b109228d2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-07f94fb3-020d-4925-8368-e75b109228d2 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":12}],"source":["# https://www.reuters.com/article/instant-article/idCAKBN2AA2WF\n","text = \"\"\"(Reuters) - Mastercard Inc said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year, joining a string of big-ticket firms that have pledged similar support.\n","\n","The credit-card giant’s announcement comes days after Elon Musk’s Tesla Inc revealed it had purchased $1.5 billion of bitcoin and would soon accept it as a form of payment.\n","\n","Asset manager BlackRock Inc and payments companies Square and PayPal have also recently backed cryptocurrencies.\n","\n","Mastercard already offers customers cards that allow people to transact using their cryptocurrencies, although without going through its network.\n","\n","\"Doing this work will create a lot more possibilities for shoppers and merchants, allowing them to transact in an entirely new form of payment. This change may open merchants up to new customers who are already flocking to digital assets,\" Mastercard said. (mstr.cd/3tLaPZM)\n","\n","Mastercard specified that not all cryptocurrencies will be supported on its network, adding that many of the hundreds of digital assets in circulation still need to tighten their compliance measures.\n","\n","Many cryptocurrencies have struggled to win the trust of mainstream investors and the general public due to their speculative nature and potential for money laundering.\n","\"\"\"\n","t5_multi['t5_transformer'].setTask('summarize ') \n","short = t5_multi.predict(text)\n","short"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"1MtQlr_8PucN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027235094,"user_tz":-300,"elapsed":18,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"8201a01a-d5a6-447b-a4f6-0ccaff753329"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original Length 1277   Summarized Length : 112 \n"," summarized text :mastercard said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year \n"]}],"source":["print(f\"Original Length {len(short.document.iloc[0])}   Summarized Length : {len(short.t5.iloc[0])} \\n summarized text :{short.t5.iloc[0]} \")\n"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"ZqOJSkrWQQA9","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1650027235097,"user_tz":-300,"elapsed":18,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"88b5646c-119b-4578-acd5-2949f4f168b6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'mastercard said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}],"source":["short.t5.iloc[0]"]},{"cell_type":"markdown","metadata":{"id":"Sd_4hzC9hz8K"},"source":["**Make sure to restart your notebook again** before starting the next section"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RQizVR2WhzTY","executionInfo":{"status":"aborted","timestamp":1650027197169,"user_tz":-300,"elapsed":26,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}}},"outputs":[],"source":["print(\"Please restart kernel if you are in google colab to free up RAM\")\n","1+'wait'\n"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"41bGg_s0ioKK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027261443,"user_tz":-300,"elapsed":18,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"67fa16f4-07fa-4b91-a169-e2c575532eb5"},"outputs":[{"output_type":"stream","name":"stdout","text":["There is only one alternative in link group java (providing /usr/bin/java): /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java\n","Nothing to configure.\n"]}],"source":["# This configures colab to use Java 8 again. \n","# You need to run this in Google colab, because after restart it likes to set Java 11 as default, which will cause issues\n","! echo 2 |  update-alternatives --config java\n"]},{"cell_type":"markdown","metadata":{"id":"PDmjkRoHhrqn"},"source":["# Translate between more than 200 Languages with  [ Microsofts Marian Models](https://marian-nmt.github.io/publications/)\n","\n","Marian is an efficient, free Neural Machine Translation framework mainly being developed by the Microsoft Translator team (646+ pretrained models & pipelines in 192+ languages)\n","You need to specify the language your data is in as `start_language` and the language you want to translate to as `target_language`.    \n"," The language references must be [ISO language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)\n","\n","`nlu.load('<start_language>.translate_to.<target_language>')`       \n","\n","**Translate Turkish to English:**     \n","`nlu.load('tr.translate_to.en')`\n","\n","**Translate English to French:**     \n","`nlu.load('en.translate_to.fr')`\n","\n","\n","**Translate French to Hebrew:**     \n","`nlu.load('fr.translate_to.he')`\n","\n","\n","\n","\n","\n","![Languages](https://camo.githubusercontent.com/b548abf3d1f9657d01fd74404354ec49fc11eea0/687474703a2f2f636b6c2d69742e64652f77702d636f6e74656e742f75706c6f6164732f323032312f30322f666c6167732e6a706567)"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"AjiWgkvQwxBy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650027266177,"user_tz":-300,"elapsed":4739,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"49fd434d-1d06-4646-80f9-567f0f4980a0"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 12:54:21--  http://ckl-it.de/wp-content/uploads/2020/12/small_btc.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 22244914 (21M) [text/csv]\n","Saving to: ‘small_btc.csv.1’\n","\n","small_btc.csv.1     100%[===================>]  21.21M  9.96MB/s    in 2.1s    \n","\n","2022-04-15 12:54:24 (9.96 MB/s) - ‘small_btc.csv.1’ saved [22244914/22244914]\n","\n"]}],"source":["import nlu\n","import pandas as pd\n","!wget http://ckl-it.de/wp-content/uploads/2020/12/small_btc.csv \n","df = pd.read_csv('/content/small_btc.csv').iloc[0:20].title"]},{"cell_type":"markdown","metadata":{"id":"Q_dx5jDkeaGO"},"source":["## Translate to German"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"_DrnIRUlXpM6","colab":{"base_uri":"https://localhost:8080/","height":760},"executionInfo":{"status":"ok","timestamp":1650027438584,"user_tz":-300,"elapsed":172459,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"56bd96c2-b7e1-4be7-d6e8-b557a1bed2e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["translate_en_de download started this may take some time.\n","Approx size to download 268 MB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                             sentence  \\\n","0      Bitcoin Price Update: Will China Lead us Down?   \n","1   Key Bitcoin Price Levels for Week 51 (15 – 22 ...   \n","2   National Australia Bank, Citing Highly Flawed ...   \n","3   Chinese Bitcoin Ban Driven by  Chinese Banking...   \n","4               Bitcoin Trade Update: Opened Position   \n","5   Key Bitcoin Price Levels for Week 52 (22 – 28 ...   \n","6                                    Bitcoin Survival   \n","7                       Massive Bitcoin Sell Going On   \n","8   Why Bitcoin will rise on Monday 23rd by more t...   \n","9         Why Bitcoin is falling, and will rise again   \n","10                              Bitcoin Price in 2014   \n","11  This is probably the best time to invest in Bi...   \n","12                     Comparing Bitcoins and Oranges   \n","13        Key Bitcoin Price Levels for the Week Ahead   \n","14  The 2014 Bitcoin War Has Started— And it is Bo...   \n","15        Two new Bitcoin ASIC Miners : Scam or Real?   \n","16        KnCMiner Announces 2TH/s Bitcoin ASIC Miner   \n","17  PSA: Do Not Fall for the Apple Bitcoin Miner Hoax   \n","18                                     Breaking News:   \n","18                        HashCows Mining Pool Hacked   \n","19  Primecoin Cloud Mining using Digital Ocean – T...   \n","\n","                                           translated  \n","0   Bitcoin Price Update: Wird China uns nach unte...  \n","1   Preisniveau für Bitcoin für Woche 51 (15 - 22 ...  \n","2   National Australia Bank, zitiert hoch abgeflac...  \n","3   Chinesische Bitcoin Ban angetrieben durch chin...  \n","4            Bitcoin Trade Update: Geöffnete Position  \n","5   Key Bitcoin Price Levels für Woche 52 (22 - 28...  \n","6                                   Bitcoin Überleben  \n","7                    Massive Bitcoin verkaufen weiter  \n","8   Warum Bitcoin am Montag um mehr als 10% steige...  \n","9            Warum Bitcoin fällt und wieder aufsteigt  \n","10                                 Bitcoin Preis 2014  \n","11  Dies ist wahrscheinlich die beste Zeit, um in ...  \n","12                 Vergleich von Bitcoins und Orangen  \n","13  Wichtige Bitcoin-Preisniveaus für die kommende...  \n","14  Der Bitcoin Krieg 2014 hat begonnen - Und es i...  \n","15     Zwei neue Bitcoin ASIC Miners: Scam oder Real?  \n","16          KnCMiner kündigt 2. Bitcoin ASIC Miner an  \n","17  PSA: Fallen Sie nicht für den Apple Bitcoin Mi...  \n","18                                       Nachrichten:  \n","18                      HashCows Bergbau Pool gehackt  \n","19  Primecoin Cloud Mining mit Digital Ocean - Der...  "],"text/html":["\n","  <div id=\"df-8ee196c8-7a9e-4800-9e4b-71800668d17a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>translated</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>Bitcoin Price Update: Wird China uns nach unte...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>Preisniveau für Bitcoin für Woche 51 (15 - 22 ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>National Australia Bank, Citing Highly Flawed ...</td>\n","      <td>National Australia Bank, zitiert hoch abgeflac...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Chinese Bitcoin Ban Driven by  Chinese Banking...</td>\n","      <td>Chinesische Bitcoin Ban angetrieben durch chin...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bitcoin Trade Update: Opened Position</td>\n","      <td>Bitcoin Trade Update: Geöffnete Position</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Key Bitcoin Price Levels for Week 52 (22 – 28 ...</td>\n","      <td>Key Bitcoin Price Levels für Woche 52 (22 - 28...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Bitcoin Survival</td>\n","      <td>Bitcoin Überleben</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Massive Bitcoin Sell Going On</td>\n","      <td>Massive Bitcoin verkaufen weiter</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Why Bitcoin will rise on Monday 23rd by more t...</td>\n","      <td>Warum Bitcoin am Montag um mehr als 10% steige...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Why Bitcoin is falling, and will rise again</td>\n","      <td>Warum Bitcoin fällt und wieder aufsteigt</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Bitcoin Price in 2014</td>\n","      <td>Bitcoin Preis 2014</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>This is probably the best time to invest in Bi...</td>\n","      <td>Dies ist wahrscheinlich die beste Zeit, um in ...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Comparing Bitcoins and Oranges</td>\n","      <td>Vergleich von Bitcoins und Orangen</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Key Bitcoin Price Levels for the Week Ahead</td>\n","      <td>Wichtige Bitcoin-Preisniveaus für die kommende...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>The 2014 Bitcoin War Has Started— And it is Bo...</td>\n","      <td>Der Bitcoin Krieg 2014 hat begonnen - Und es i...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Two new Bitcoin ASIC Miners : Scam or Real?</td>\n","      <td>Zwei neue Bitcoin ASIC Miners: Scam oder Real?</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>KnCMiner Announces 2TH/s Bitcoin ASIC Miner</td>\n","      <td>KnCMiner kündigt 2. Bitcoin ASIC Miner an</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>PSA: Do Not Fall for the Apple Bitcoin Miner Hoax</td>\n","      <td>PSA: Fallen Sie nicht für den Apple Bitcoin Mi...</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Breaking News:</td>\n","      <td>Nachrichten:</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>HashCows Mining Pool Hacked</td>\n","      <td>HashCows Bergbau Pool gehackt</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Primecoin Cloud Mining using Digital Ocean – T...</td>\n","      <td>Primecoin Cloud Mining mit Digital Ocean - Der...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ee196c8-7a9e-4800-9e4b-71800668d17a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-8ee196c8-7a9e-4800-9e4b-71800668d17a button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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"],"text/html":["\n","  <div id=\"df-f29358eb-fba0-4d7c-bdbf-dae13947ad80\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>translated</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bitcoin Price Update: Will China Lead us Down?</td>\n","      <td>Bitcoin 价格最新消息:中国会带领我们下台吗 ? . . . . . . . . . . .</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Key Bitcoin Price Levels for Week 51 (15 – 22 ...</td>\n","      <td>第51周(12月15 - 22日) Bitcoin 关键价格 水平 。 12月 15 - 2...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>National Australia Bank, Citing Highly Flawed ...</td>\n","      <td>国家澳大利亚银行, 援引高易燃数据, 称 Bitcoin 是一个泡泡 。 Name UN C...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Chinese Bitcoin Ban Driven by  Chinese Banking...</td>\n","      <td>被中国银行危机驱赶的中国 Bitcoin Ban ? ? ? 。 。 。 。 。 。 。 。 。</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bitcoin Trade Update: Opened Position</td>\n","      <td>Bittcoin 贸易最新贸易 : 开放位置 : 开放位置 Name Name 开放位置 N...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Key Bitcoin Price Levels for Week 52 (22 – 28 ...</td>\n","      <td>12月22 - 28日 - 我的贸易计划 - Bitcoin 价格第52周( 12月 22 ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Bitcoin Survival</td>\n","      <td>Bitcoin 生存 毕 生 活 生 生 业 业 业 业 业 业 业 业 业 业 业 业</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Massive Bitcoin Sell Going On</td>\n","      <td>大规模 Bittcoin 卖 卖 上 上 上 上 上 上 上 上 上 上 上 上 上 上</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Why Bitcoin will rise on Monday 23rd by more t...</td>\n","      <td>为何比特币 会在 23 日星期一 上升 超过 10% 的 比例 , 超过 10% 的 比例 ...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Why Bitcoin is falling, and will rise again</td>\n","      <td>为何比特币 跌了 , 还会再升 , 何必 跌 , 何必 , 何 跌 , 何 跌 ,</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Bitcoin Price in 2014</td>\n","      <td>2014年比特币价格 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>This is probably the best time to invest in Bi...</td>\n","      <td>这可能是 投资 Bitcoin 的最佳时机 。 。 。 。 。 。 。 。 。 。 。 。 ...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Comparing Bitcoins and Oranges</td>\n","      <td>比较比特币 橙 和 橙 的 比较 . . . . . . . . . . . .</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Key Bitcoin Price Levels for the Week Ahead</td>\n","      <td>提前一周的 Bitcoin 关键价格水平 。 Name IP IP IP IP IP IP ...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>The 2014 Bitcoin War Has Started— And it is Bo...</td>\n","      <td>4 样东西, 足以 备战 的 4 样东西 , 4 样东西 , 4 样东西 , 4 样东西</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Two new Bitcoin ASIC Miners : Scam or Real?</td>\n","      <td>两种新的 Bitcoin ASIC ASIC 矿工 : 斯卡姆 or Real ? , 或 ...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>KnCMiner Announces 2TH/s Bitcoin ASIC Miner</td>\n","      <td>宣布 Bitcoin ASIC 矿工 , 即 2- TH / / / 的 Bitcoin A...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>PSA: Do Not Fall for the Apple Bitcoin Miner Hoax</td>\n","      <td>美 美 美 子 : 苹果 Bitcoin 矿 矿 矿 山 山 山 山 山 , 不可 倒 掉</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Breaking News:</td>\n","      <td>断层新闻 : 校对:Soup {\\fn微软雅黑\\fs16\\2cHFFFFFF\\3cH4D00...</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>HashCows Mining Pool Hacked</td>\n","      <td>混 集 混 作 入 入 入 了 的 油 牛 矿 池 池 , 被 洗 入 了 .</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Primecoin Cloud Mining using Digital Ocean – T...</td>\n","      <td>利用数字海洋开采原金云 - 完整指南 - 完整指南 - 完整指南 - 利用数字海洋开采原金云...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f29358eb-fba0-4d7c-bdbf-dae13947ad80')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f29358eb-fba0-4d7c-bdbf-dae13947ad80 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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और यह है बॉबी ...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Two new Bitcoin ASIC Miners : Scam or Real?</td>\n","      <td>दो नए बिट बिटपरन</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>KnCMiner Announces 2TH/s Bitcoin ASIC Miner</td>\n","      <td>केनसीममंर घोषणा 2 माह/snin Mer</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>PSA: Do Not Fall for the Apple Bitcoin Miner Hoax</td>\n","      <td>PSA: एपीपेर होक्स के लिए मत गिरिए</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Breaking News:</td>\n","      <td>समाचार ब्रेकिंगः</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>HashCows Mining Pool Hacked</td>\n","      <td>हैश- बस्स पूलिंग</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Primecoin Cloud Mining using Digital Ocean – T...</td>\n","      <td>डिजिटल महासागर - पूर्ण मार्गदर्शक का प्रयोग कर...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-360dd875-0ab0-4b9a-af91-c9e1d4578af4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-360dd875-0ab0-4b9a-af91-c9e1d4578af4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":5}],"source":["translate_pipe = nlu.load('en.translate_to.hi')\n","translate_pipe.predict(df)"]},{"cell_type":"markdown","metadata":{"id":"50Ap0BIujDWr"},"source":["# Train a Multi Lingual Classifier for 100+ languages from a dataset with just one language\n","\n","[Leverage Language-agnostic BERT Sentence Embedding (LABSE)​ and acheive state of the art!](https://arxiv.org/abs/2007.01852) ​  ​  \n","\n","Training a classifier with LABSE embeddings enables the knowledge to be transferred to 109 languages!\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator)  from Spark NLP you can achieve State Of the Art results on any binary class text classification problem.\n","\n","### Languages suppoted by LABSE\n","![labse languages](http://ckl-it.de/wp-content/uploads/2021/02/LABSE.png)\n","\n"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"S0Eoos30h8aV","colab":{"base_uri":"https://localhost:8080/","height":200},"executionInfo":{"status":"error","timestamp":1650027895603,"user_tz":-300,"elapsed":43,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"bef9cd6d-f3d6-484d-be6c-8a3d40acf039"},"outputs":[{"output_type":"stream","name":"stdout","text":["Please restart kernel if you are in google colab to free up RAM\n"]},{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-6-eb226dd89e38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please restart kernel if you are in google colab to free up RAM\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'wait'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"]}],"source":["print(\"Please restart kernel if you are in google colab to free up RAM\")\n","1+'wait'\n"]},{"cell_type":"markdown","metadata":{"id":"XvgCKMyEZuQo"},"source":["# Train a Multi Lingual Classifier for 100+ languages from a dataset with just one language\n","\n","[Leverage Language-agnostic BERT Sentence Embedding (LABSE)​ and acheive state of the art!](https://arxiv.org/abs/2007.01852) ​  ​  \n","\n","Training a classifier with LABSE embeddings enables the knowledge to be transferred to 109 languages!\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator)  from Spark NLP you can achieve State Of the Art results on any binary class text classification problem.\n","\n","### Languages suppoted by LABSE\n","![labse languages](http://ckl-it.de/wp-content/uploads/2021/02/LABSE.png)\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":614},"id":"y4xSRWIhwT28","outputId":"d6fbd4ed-a75b-4bf3-c3b3-955fa0d8e31e","executionInfo":{"status":"ok","timestamp":1650025510538,"user_tz":-300,"elapsed":2127,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["import nlu\n","# Download French twitter  Sentiment dataset  https://www.kaggle.com/hbaflast/french-twitter-sentiment-analysis\n","! wget http://ckl-it.de/wp-content/uploads/2021/02/french_tweets.csv\n","\n","import pandas as pd\n","\n","train_path = '/content/french_tweets.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df = train_df.sample(frac=1).reset_index(drop=True)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 12:25:07--  http://ckl-it.de/wp-content/uploads/2021/02/french_tweets.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 10237264 (9.8M) [text/csv]\n","Saving to: ‘french_tweets.csv.1’\n","\n","french_tweets.csv.1 100%[===================>]   9.76M  53.5MB/s    in 0.2s    \n","\n","2022-04-15 12:25:07 (53.5 MB/s) - ‘french_tweets.csv.1’ saved [10237264/10237264]\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["                                                    text         y\n","0      Grande plateforme de cuisine en bois plate 120...  negative\n","1                                    quelqu'un me manque  negative\n","2      Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...  positive\n","3      La semaine de travail commence par une cheminÃ...  negative\n","4      Omg matteo becucci a gagnÃ© le facteur x !! Un...  positive\n","...                                                  ...       ...\n","99995                              attends quoi? malade?  negative\n","99996  P.s. Bonne chance Ã  la tournÃ©e! J'aimerais p...  positive\n","99997  Lol, pas d'endroit oÃ¹ je peux te trouver lol,...  negative\n","99998  Nous ne pouvons pas trouver notre copie! Maint...  negative\n","99999                                 - voici les filles  positive\n","\n","[100000 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-31916f5a-ca9e-4a91-96ff-91813ca22642\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grande plateforme de cuisine en bois plate 120...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>quelqu'un me manque</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>La semaine de travail commence par une cheminÃ...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Omg matteo becucci a gagnÃ© le facteur x !! Un...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>99995</th>\n","      <td>attends quoi? malade?</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>99996</th>\n","      <td>P.s. Bonne chance Ã  la tournÃ©e! J'aimerais p...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>99997</th>\n","      <td>Lol, pas d'endroit oÃ¹ je peux te trouver lol,...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>99998</th>\n","      <td>Nous ne pouvons pas trouver notre copie! Maint...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>99999</th>\n","      <td>- voici les filles</td>\n","      <td>positive</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>100000 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31916f5a-ca9e-4a91-96ff-91813ca22642')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-31916f5a-ca9e-4a91-96ff-91813ca22642 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-31916f5a-ca9e-4a91-96ff-91813ca22642');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":1}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["## Train Deep Learning Classifier using `nlu.load('train.sentiment')`\n","\n","Al you need is a Pandas Dataframe with a label column named `y` and the column with text data should be named `text`\n","\n","We are training on a french dataset and can then predict classes correct **in 100+ langauges**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":850},"id":"mptfvHx-MMMX","outputId":"3463d7d2-3e70-46af-fd76-ce6a8f42f06d","executionInfo":{"status":"ok","timestamp":1650025888720,"user_tz":-300,"elapsed":373025,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["from sklearn.metrics import classification_report\n","# Train longer!\n","trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","trainable_pipe['trainable_sentiment_dl'].setMaxEpochs(60)  \n","trainable_pipe['trainable_sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:2000])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:2000],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","              precision    recall  f1-score   support\n","\n","    negative       0.90      0.89      0.89       994\n","    positive       0.89      0.90      0.90      1006\n","\n","    accuracy                           0.89      2000\n","   macro avg       0.89      0.89      0.89      2000\n","weighted avg       0.89      0.89      0.89      2000\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["                                               document  \\\n","0     Grande plateforme de cuisine en bois plate 120...   \n","1                                   quelqu'un me manque   \n","2     Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...   \n","3     La semaine de travail commence par une cheminÃ...   \n","4     Omg matteo becucci a gagnÃ© le facteur x !! Un...   \n","...                                                 ...   \n","1995                  as! Je vais tenter d'Ãªtre lÃ  xx   \n","1996  Bon. Je vais Ã©crire cela sur ma liste des qua...   \n","1997                     bonne chance. tellement mignon   \n","1998                         heureux a eminem est Baack   \n","1999  Ugh ma vie est ruinÃ©e, andy Roddick est maint...   \n","\n","                               sentence_embedding_labse sentiment  \\\n","0     [0.02403962053358555, 0.03160339966416359, -0....  negative   \n","1     [-0.03878656029701233, -0.04454658553004265, -...  negative   \n","2     [-0.030596556141972542, -0.051767248660326004,...  positive   \n","3     [0.0500025749206543, 0.006966877728700638, -0....  negative   \n","4     [-0.010731207206845284, -0.036207739263772964,...  positive   \n","...                                                 ...       ...   \n","1995  [-0.047317251563072205, -0.07227267324924469, ...  negative   \n","1996  [0.006470187567174435, -0.03349378705024719, -...  positive   \n","1997  [-0.04401470348238945, -0.0692373588681221, -0...  positive   \n","1998  [0.03480768948793411, -0.03484829515218735, -0...  positive   \n","1999  [-0.07050205767154694, -0.061325717717409134, ...  negative   \n","\n","     sentiment_confidence                                               text  \\\n","0                0.991941  Grande plateforme de cuisine en bois plate 120...   \n","1                0.999991                                quelqu'un me manque   \n","2                     1.0  Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...   \n","3                0.975401  La semaine de travail commence par une cheminÃ...   \n","4                0.987098  Omg matteo becucci a gagnÃ© le facteur x !! Un...   \n","...                   ...                                                ...   \n","1995             0.997622                  as! Je vais tenter d'Ãªtre lÃ  xx   \n","1996             0.999998  Bon. Je vais Ã©crire cela sur ma liste des qua...   \n","1997                  1.0                     bonne chance. tellement mignon   \n","1998                  1.0                         heureux a eminem est Baack   \n","1999             0.999982  Ugh ma vie est ruinÃ©e, andy Roddick est maint...   \n","\n","             y  \n","0     negative  \n","1     negative  \n","2     positive  \n","3     negative  \n","4     positive  \n","...        ...  \n","1995  positive  \n","1996  positive  \n","1997  positive  \n","1998  positive  \n","1999  negative  \n","\n","[2000 rows x 6 columns]"],"text/html":["\n","  <div id=\"df-ee4f4540-acf8-4daa-982b-9a65664e7487\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","      <th>text</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grande plateforme de cuisine en bois plate 120...</td>\n","      <td>[0.02403962053358555, 0.03160339966416359, -0....</td>\n","      <td>negative</td>\n","      <td>0.991941</td>\n","      <td>Grande plateforme de cuisine en bois plate 120...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>quelqu'un me manque</td>\n","      <td>[-0.03878656029701233, -0.04454658553004265, -...</td>\n","      <td>negative</td>\n","      <td>0.999991</td>\n","      <td>quelqu'un me manque</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...</td>\n","      <td>[-0.030596556141972542, -0.051767248660326004,...</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","      <td>Merveilleuse soirÃ©e Ã  la nouvelle annÃ©e nap...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>La semaine de travail commence par une cheminÃ...</td>\n","      <td>[0.0500025749206543, 0.006966877728700638, -0....</td>\n","      <td>negative</td>\n","      <td>0.975401</td>\n","      <td>La semaine de travail commence par une cheminÃ...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Omg matteo becucci a gagnÃ© le facteur x !! Un...</td>\n","      <td>[-0.010731207206845284, -0.036207739263772964,...</td>\n","      <td>positive</td>\n","      <td>0.987098</td>\n","      <td>Omg matteo becucci a gagnÃ© le facteur x !! Un...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1995</th>\n","      <td>as! Je vais tenter d'Ãªtre lÃ  xx</td>\n","      <td>[-0.047317251563072205, -0.07227267324924469, ...</td>\n","      <td>negative</td>\n","      <td>0.997622</td>\n","      <td>as! Je vais tenter d'Ãªtre lÃ  xx</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>1996</th>\n","      <td>Bon. Je vais Ã©crire cela sur ma liste des qua...</td>\n","      <td>[0.006470187567174435, -0.03349378705024719, -...</td>\n","      <td>positive</td>\n","      <td>0.999998</td>\n","      <td>Bon. Je vais Ã©crire cela sur ma liste des qua...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>1997</th>\n","      <td>bonne chance. tellement mignon</td>\n","      <td>[-0.04401470348238945, -0.0692373588681221, -0...</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","      <td>bonne chance. tellement mignon</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>heureux a eminem est Baack</td>\n","      <td>[0.03480768948793411, -0.03484829515218735, -0...</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","      <td>heureux a eminem est Baack</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>Ugh ma vie est ruinÃ©e, andy Roddick est maint...</td>\n","      <td>[-0.07050205767154694, -0.061325717717409134, ...</td>\n","      <td>negative</td>\n","      <td>0.999982</td>\n","      <td>Ugh ma vie est ruinÃ©e, andy Roddick est maint...</td>\n","      <td>negative</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2000 rows × 6 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4f4540-acf8-4daa-982b-9a65664e7487')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ee4f4540-acf8-4daa-982b-9a65664e7487 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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[0.03472337871789932, -0.06212150678038597, -0...   \n","\n","  sentiment sentiment_confidence  \n","0  positive             0.880137  "],"text/html":["\n","  <div id=\"df-33792823-c361-435e-b990-cee7ef1976d4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>This was awful!</td>\n","      <td>[0.03472337871789932, -0.06212150678038597, -0...</td>\n","      <td>positive</td>\n","      <td>0.880137</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33792823-c361-435e-b990-cee7ef1976d4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-33792823-c361-435e-b990-cee7ef1976d4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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[0.038682401180267334, -0.05859256163239479, -...   \n","\n","  sentiment sentiment_confidence  \n","0  positive                  1.0  "],"text/html":["\n","  <div id=\"df-32238059-6443-4bb0-a8a9-40bfba9ccbc5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>This was great!</td>\n","      <td>[0.038682401180267334, -0.05859256163239479, -...</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32238059-6443-4bb0-a8a9-40bfba9ccbc5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-32238059-6443-4bb0-a8a9-40bfba9ccbc5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["#### The Model understands German\n","![de](https://www.worldometers.info/img/flags/small/tn_gm-flag.gif)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"dzaaZrI4tVWc","outputId":"37a560fc-191b-42b2-d2f7-1ac24b2a78c4","executionInfo":{"status":"ok","timestamp":1650025893689,"user_tz":-300,"elapsed":963,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# German for:' this movie was great!'\n","fitted_pipe.predict(\"Der Film war echt klasse!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    sentence  \\\n","0  Der Film war echt klasse!   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.01518925093114376, -0.048348318785429, -0....  positive   \n","\n","  sentiment_confidence  \n","0             0.999996  "],"text/html":["\n","  <div id=\"df-7cd6bbf0-bd05-494e-b354-a76701c0acb7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Der Film war echt klasse!</td>\n","      <td>[-0.01518925093114376, -0.048348318785429, -0....</td>\n","      <td>positive</td>\n","      <td>0.999996</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7cd6bbf0-bd05-494e-b354-a76701c0acb7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-7cd6bbf0-bd05-494e-b354-a76701c0acb7 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"BbhgTSBGtTtJ","outputId":"46f885a8-9743-464c-978c-dd1d396cbd6f","executionInfo":{"status":"ok","timestamp":1650025893697,"user_tz":-300,"elapsed":110,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# German for: 'This movie was really boring'\n","fitted_pipe.predict(\"Der Film war echt langweilig!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        sentence  \\\n","0  Der Film war echt langweilig!   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.013573330827057362, -0.05144454538822174, ...  negative   \n","\n","  sentiment_confidence  \n","0             0.999177  "],"text/html":["\n","  <div id=\"df-79676b60-b0ae-4b5f-be5b-3316c69db73e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Der Film war echt langweilig!</td>\n","      <td>[-0.013573330827057362, -0.05144454538822174, ...</td>\n","      <td>negative</td>\n","      <td>0.999177</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79676b60-b0ae-4b5f-be5b-3316c69db73e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-79676b60-b0ae-4b5f-be5b-3316c69db73e button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["#### The Model understands Chinese\n","![zh](https://www.worldometers.info/img/flags/small/tn_ch-flag.gif)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"kYSYqtoRtc-P","outputId":"2edbedad-8874-4348-c192-4c75fe6c5477","executionInfo":{"status":"ok","timestamp":1650025894294,"user_tz":-300,"elapsed":697,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# Chinese for: \"This model was awful!\"\n","fitted_pipe.predict(\"这部电影太糟糕了！\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    sentence                           sentence_embedding_labse sentiment  \\\n","0  这部电影太糟糕了！  [-0.05075531825423241, -0.061598796397447586, ...  negative   \n","\n","  sentiment_confidence  \n","0             0.999964  "],"text/html":["\n","  <div id=\"df-fc97f632-c6a1-4b9e-8980-f2092436ea14\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>这部电影太糟糕了！</td>\n","      <td>[-0.05075531825423241, -0.061598796397447586, ...</td>\n","      <td>negative</td>\n","      <td>0.999964</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc97f632-c6a1-4b9e-8980-f2092436ea14')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-fc97f632-c6a1-4b9e-8980-f2092436ea14 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"06v9SD-QtlBU","outputId":"2c41e10c-3787-4919-fbda-771af29d83d1","executionInfo":{"status":"ok","timestamp":1650025894296,"user_tz":-300,"elapsed":69,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# Chine for : \"This move was great!\"\n","fitted_pipe.predict(\"此举很棒！\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  sentence                           sentence_embedding_labse sentiment  \\\n","0    此举很棒！  [0.026034904643893242, -0.065867118537426, -0....  positive   \n","\n","  sentiment_confidence  \n","0                  1.0  "],"text/html":["\n","  <div id=\"df-6d0ebf5b-0ad6-4d8c-b6b6-4c11d604e2f0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>此举很棒！</td>\n","      <td>[0.026034904643893242, -0.065867118537426, -0....</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d0ebf5b-0ad6-4d8c-b6b6-4c11d604e2f0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-6d0ebf5b-0ad6-4d8c-b6b6-4c11d604e2f0 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["#### Model understanda Afrikaans\n","\n","![af](https://www.worldometers.info/img/flags/small/tn_sf-flag.gif)\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"VMPhbgw9twtf","outputId":"3435ee55-5a07-4c6c-e848-9058b47991df","executionInfo":{"status":"ok","timestamp":1650025894941,"user_tz":-300,"elapsed":712,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# Afrikaans for 'This movie was amazing!'\n","fitted_pipe.predict(\"Hierdie film was ongelooflik!\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        sentence  \\\n","0  Hierdie film was ongelooflik!   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.019771261140704155, -0.02687734179198742, ...  positive   \n","\n","  sentiment_confidence  \n","0             0.999981  "],"text/html":["\n","  <div id=\"df-fa8ecf18-46eb-4bf1-94fe-5a6900a60696\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Hierdie film was ongelooflik!</td>\n","      <td>[-0.019771261140704155, -0.02687734179198742, ...</td>\n","      <td>positive</td>\n","      <td>0.999981</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa8ecf18-46eb-4bf1-94fe-5a6900a60696')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-fa8ecf18-46eb-4bf1-94fe-5a6900a60696 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-fa8ecf18-46eb-4bf1-94fe-5a6900a60696');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"zWgNTIdkumhX","outputId":"59d07566-bb4f-4598-8999-fe53de8b0af2","executionInfo":{"status":"ok","timestamp":1650025894943,"user_tz":-300,"elapsed":105,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# Afrikaans for :'The movie made me fall asleep, it's awful!'\n","fitted_pipe.predict('Die film het my aan die slaap laat raak, dit is verskriklik!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                            sentence  \\\n","0  Die film het my aan die slaap laat raak, dit i...   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.050655145198106766, -0.017065493389964104,...   neutral   \n","\n","  sentiment_confidence  \n","0             0.567614  "],"text/html":["\n","  <div id=\"df-097a4e1b-1c73-4274-84a0-12dc8f29dabf\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Die film het my aan die slaap laat raak, dit i...</td>\n","      <td>[-0.050655145198106766, -0.017065493389964104,...</td>\n","      <td>neutral</td>\n","      <td>0.567614</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-097a4e1b-1c73-4274-84a0-12dc8f29dabf')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-097a4e1b-1c73-4274-84a0-12dc8f29dabf button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["#### The model understands Vietnamese\n","![vi](https://www.worldometers.info/img/flags/small/tn_vm-flag.gif)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"wCcTS5gIu511","outputId":"65138c4d-d34b-4648-d7dc-3cda4631b1c5","executionInfo":{"status":"ok","timestamp":1650025895697,"user_tz":-300,"elapsed":857,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["# Vietnamese for : 'The movie was painful to watch'\n","fitted_pipe.predict('Phim đau điếng người xem')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   sentence  \\\n","0  Phim đau điếng người xem   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.05414639413356781, 0.04168793931603432, -0...  negative   \n","\n","  sentiment_confidence  \n","0             0.999989  "],"text/html":["\n","  <div id=\"df-9d4a9975-c6bd-4033-afdf-d6e7749d3066\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Phim đau điếng người xem</td>\n","      <td>[-0.05414639413356781, 0.04168793931603432, -0...</td>\n","      <td>negative</td>\n","      <td>0.999989</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d4a9975-c6bd-4033-afdf-d6e7749d3066')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-9d4a9975-c6bd-4033-afdf-d6e7749d3066 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"M6giDPK-wm2G","outputId":"113333f1-fad8-4ca8-f60f-70756e4ee237","executionInfo":{"status":"ok","timestamp":1650025895705,"user_tz":-300,"elapsed":117,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["\n","# Vietnamese for : 'This was the best movie ever'\n","fitted_pipe.predict('Đây là bộ phim hay nhất từ ​​trước đến nay')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                     sentence  \\\n","0  Đây là bộ phim hay nhất từ ​​trước đến nay   \n","\n","                            sentence_embedding_labse sentiment  \\\n","0  [-0.035795826464891434, -0.0058449129574000835...  positive   \n","\n","  sentiment_confidence  \n","0             0.997236  "],"text/html":["\n","  <div id=\"df-b2e31c1b-f95b-4543-aa83-8807f98e78de\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Đây là bộ phim hay nhất từ ​​trước đến nay</td>\n","      <td>[-0.035795826464891434, -0.0058449129574000835...</td>\n","      <td>positive</td>\n","      <td>0.997236</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2e31c1b-f95b-4543-aa83-8807f98e78de')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b2e31c1b-f95b-4543-aa83-8807f98e78de button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["#### The model understands Japanese\n","![ja](https://www.worldometers.info/img/flags/small/tn_ja-flag.gif)\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"1IfJu3q8wwUt","outputId":"c411d2e7-8137-4fd9-cf75-256948d4a57d","executionInfo":{"status":"ok","timestamp":1650025896306,"user_tz":-300,"elapsed":713,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["\n","# Japanese for : 'This is now my favorite movie!'\n","fitted_pipe.predict('これが私のお気に入りの映画です！')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           sentence                           sentence_embedding_labse  \\\n","0  これが私のお気に入りの映画です！  [-0.006344371009618044, -0.03161681815981865, ...   \n","\n","  sentiment sentiment_confidence  \n","0  positive                  1.0  "],"text/html":["\n","  <div id=\"df-162651b8-d422-4184-a1f4-d5ba3c253d0b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>これが私のお気に入りの映画です！</td>\n","      <td>[-0.006344371009618044, -0.03161681815981865, ...</td>\n","      <td>positive</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-162651b8-d422-4184-a1f4-d5ba3c253d0b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-162651b8-d422-4184-a1f4-d5ba3c253d0b button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"h3k7_PFhxOve","outputId":"a3e3f282-278b-4683-a72e-2c855de2516f","executionInfo":{"status":"ok","timestamp":1650025896315,"user_tz":-300,"elapsed":77,"user":{"displayName":"Gammer Otaku","userId":"18042713576744284398"}}},"source":["\n","# Japanese for : 'I would rather kill myself than watch that movie again'\n","fitted_pipe.predict('その映画をもう一度見るよりも自殺したい')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              sentence                           sentence_embedding_labse  \\\n","0  その映画をもう一度見るよりも自殺したい  [-0.04823155328631401, -0.036920782178640366, ...   \n","\n","  sentiment sentiment_confidence  \n","0  negative             0.999938  "],"text/html":["\n","  <div id=\"df-c2efce6a-e1d6-46a5-bb2f-acd42935b487\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence</th>\n","      <th>sentence_embedding_labse</th>\n","      <th>sentiment</th>\n","      <th>sentiment_confidence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>その映画をもう一度見るよりも自殺したい</td>\n","      <td>[-0.04823155328631401, -0.036920782178640366, ...</td>\n","      <td>negative</td>\n","      <td>0.999938</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2efce6a-e1d6-46a5-bb2f-acd42935b487')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c2efce6a-e1d6-46a5-bb2f-acd42935b487 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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